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updated benchmarks, fix start-vllm

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Este commit está contenido en:
Donato Capitella
2026-02-23 19:39:19 +00:00
padre e0fadf426b
commit e726d406fa
Se han modificado 59 ficheros con 886 adiciones y 309 borrados
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benchmarks/benchmark_results/Qwen_Qwen3-14B-AWQ_cluster_tp2_eth_throughput.json
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benchmarks/benchmark_results/Qwen_Qwen3-14B-AWQ_cluster_tp2_throughput.json
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benchmarks/benchmark_results/Qwen_Qwen3-14B-AWQ_tp1_throughput.json
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benchmarks/benchmark_results/btbtyler09_Qwen3-Coder-30B-A3B-Instruct-gptq-4bit_cluster_tp2_eth_throughput.json
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benchmarks/benchmark_results/btbtyler09_Qwen3-Coder-30B-A3B-Instruct-gptq-4bit_cluster_tp2_throughput.json
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benchmarks/benchmark_results/btbtyler09_Qwen3-Coder-30B-A3B-Instruct-gptq-4bit_tp1_throughput.json
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benchmarks/benchmark_results/btbtyler09_Qwen3-Coder-30B-A3B-Instruct-gptq-8bit_cluster_tp2_eth_throughput.json
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benchmarks/benchmark_results/btbtyler09_Qwen3-Coder-30B-A3B-Instruct-gptq-8bit_cluster_tp2_throughput.json
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benchmarks/benchmark_results/btbtyler09_Qwen3-Coder-30B-A3B-Instruct-gptq-8bit_tp1_throughput.json
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benchmarks/benchmark_results/dazipe_Qwen3-Next-80B-A3B-Instruct-GPTQ-Int4A16_cluster_tp2_eth_throughput.json
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benchmarks/benchmark_results/dazipe_Qwen3-Next-80B-A3B-Instruct-GPTQ-Int4A16_cluster_tp2_throughput.json
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benchmarks/benchmark_results/dazipe_Qwen3-Next-80B-A3B-Instruct-GPTQ-Int4A16_tp1_throughput.json
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benchmarks/benchmark_results/google_gemma-3-12b-it_cluster_tp2_eth_throughput.json
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benchmarks/benchmark_results/google_gemma-3-12b-it_cluster_tp2_throughput.json
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benchmarks/benchmark_results/google_gemma-3-12b-it_tp1_throughput.json
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benchmarks/benchmark_results/meta-llama_Meta-Llama-3.1-8B-Instruct_cluster_tp2_eth_throughput.json
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benchmarks/benchmark_results/meta-llama_Meta-Llama-3.1-8B-Instruct_cluster_tp2_throughput.json
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benchmarks/benchmark_results/meta-llama_Meta-Llama-3.1-8B-Instruct_tp1_throughput.json
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benchmarks/benchmark_results/mratsim_MiniMax-M2.5-BF16-INT4-AWQ_cluster_tp2_eth_throughput.json
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benchmarks/benchmark_results/mratsim_MiniMax-M2.5-BF16-INT4-AWQ_cluster_tp2_throughput.json
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benchmarks/benchmark_results/openai_gpt-oss-120b_cluster_tp2_eth_throughput.json
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benchmarks/benchmark_results/openai_gpt-oss-120b_cluster_tp2_throughput.json
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benchmarks/benchmark_results/openai_gpt-oss-120b_tp1_throughput.json
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benchmarks/benchmark_results/openai_gpt-oss-20b_cluster_tp2_eth_throughput.json
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benchmarks/benchmark_results/openai_gpt-oss-20b_cluster_tp2_throughput.json
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benchmarks/benchmark_results/openai_gpt-oss-20b_tp1_throughput.json
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benchmarks/benchmark_results/zai-org_GLM-4.7-Flash_cluster_tp2_eth_throughput.json
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benchmarks/benchmark_results/zai-org_GLM-4.7-Flash_cluster_tp2_throughput.json
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benchmarks/benchmark_results/zai-org_GLM-4.7-Flash_tp1_throughput.json
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benchmarks/benchmark_results_rocm/Qwen_Qwen3-14B-AWQ_cluster_tp2_eth_throughput.json
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benchmarks/benchmark_results_rocm/Qwen_Qwen3-14B-AWQ_cluster_tp2_throughput.json
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benchmarks/benchmark_results_rocm/Qwen_Qwen3-14B-AWQ_tp1_throughput.json
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benchmarks/benchmark_results_rocm/btbtyler09_Qwen3-Coder-30B-A3B-Instruct-gptq-4bit_cluster_tp2_eth_throughput.json
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benchmarks/benchmark_results_rocm/btbtyler09_Qwen3-Coder-30B-A3B-Instruct-gptq-4bit_cluster_tp2_throughput.json
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benchmarks/benchmark_results_rocm/btbtyler09_Qwen3-Coder-30B-A3B-Instruct-gptq-4bit_tp1_throughput.json
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benchmarks/benchmark_results_rocm/btbtyler09_Qwen3-Coder-30B-A3B-Instruct-gptq-8bit_cluster_tp2_eth_throughput.json
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benchmarks/benchmark_results_rocm/btbtyler09_Qwen3-Coder-30B-A3B-Instruct-gptq-8bit_cluster_tp2_throughput.json
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benchmarks/benchmark_results_rocm/btbtyler09_Qwen3-Coder-30B-A3B-Instruct-gptq-8bit_tp1_throughput.json
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benchmarks/benchmark_results_rocm/dazipe_Qwen3-Next-80B-A3B-Instruct-GPTQ-Int4A16_cluster_tp2_eth_throughput.json
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benchmarks/benchmark_results_rocm/dazipe_Qwen3-Next-80B-A3B-Instruct-GPTQ-Int4A16_cluster_tp2_throughput.json
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benchmarks/benchmark_results_rocm/dazipe_Qwen3-Next-80B-A3B-Instruct-GPTQ-Int4A16_tp1_throughput.json
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benchmarks/benchmark_results_rocm/google_gemma-3-12b-it_cluster_tp2_eth_throughput.json
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benchmarks/benchmark_results_rocm/google_gemma-3-12b-it_cluster_tp2_throughput.json
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benchmarks/benchmark_results_rocm/google_gemma-3-12b-it_tp1_throughput.json
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benchmarks/benchmark_results_rocm/meta-llama_Meta-Llama-3.1-8B-Instruct_cluster_tp2_eth_throughput.json
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benchmarks/benchmark_results_rocm/meta-llama_Meta-Llama-3.1-8B-Instruct_cluster_tp2_throughput.json
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{
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"elapsed_time": 224.3753512299736,
"num_requests": 200,
"total_num_tokens": 145877,
"requests_per_second": 0.8898289873490544,
"tokens_per_second": 649.02791593759
"requests_per_second": 0.8913635071929533,
"tokens_per_second": 650.1471716939323
}
benchmarks/benchmark_results_rocm/meta-llama_Meta-Llama-3.1-8B-Instruct_tp1_throughput.json
+3 -3
Ver fichero
@@ -1,7 +1,7 @@
{
"elapsed_time": 322.171811016,
"elapsed_time": 336.45260514499387,
"num_requests": 200,
"total_num_tokens": 145877,
"requests_per_second": 0.620786776376495,
"tokens_per_second": 452.7925628873698
"requests_per_second": 0.5944373648520577,
"tokens_per_second": 433.5736973626181
}
benchmarks/benchmark_results_rocm/mratsim_MiniMax-M2.5-BF16-INT4-AWQ_cluster_tp2_eth_throughput.json
+7
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@@ -0,0 +1,7 @@
{
"elapsed_time": 1484.8385301349917,
"num_requests": 200,
"total_num_tokens": 146523,
"requests_per_second": 0.1346947805710681,
"tokens_per_second": 98.67941666807306
}
benchmarks/benchmark_results_rocm/openai_gpt-oss-120b_cluster_tp2_eth_throughput.json
+7
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@@ -0,0 +1,7 @@
{
"elapsed_time": 1707.9124416089617,
"num_requests": 200,
"total_num_tokens": 147036,
"requests_per_second": 0.11710202181769186,
"tokens_per_second": 86.0910643999307
}
benchmarks/benchmark_results_rocm/openai_gpt-oss-120b_cluster_tp2_throughput.json
+3 -3
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@@ -1,7 +1,7 @@
{
"elapsed_time": 1315.035868578001,
"elapsed_time": 1242.463667072996,
"num_requests": 200,
"total_num_tokens": 147036,
"requests_per_second": 0.15208710635115047,
"tokens_per_second": 111.8113988472388
"requests_per_second": 0.16097050183460196,
"tokens_per_second": 118.34229353876268
}
benchmarks/benchmark_results_rocm/openai_gpt-oss-120b_tp1_throughput.json
+3 -3
Ver fichero
@@ -1,7 +1,7 @@
{
"elapsed_time": 1923.4690410719995,
"elapsed_time": 1966.935257990961,
"num_requests": 200,
"total_num_tokens": 147036,
"requests_per_second": 0.10397879858182421,
"tokens_per_second": 76.44313314138553
"requests_per_second": 0.10168102848706935,
"tokens_per_second": 74.75385852312364
}
benchmarks/benchmark_results_rocm/openai_gpt-oss-20b_cluster_tp2_eth_throughput.json
+7
Ver fichero
@@ -0,0 +1,7 @@
{
"elapsed_time": 299.5004001749912,
"num_requests": 200,
"total_num_tokens": 147036,
"requests_per_second": 0.6677787404729495,
"tokens_per_second": 490.93757442090305
}
benchmarks/benchmark_results_rocm/openai_gpt-oss-20b_cluster_tp2_throughput.json
+3 -3
Ver fichero
@@ -1,7 +1,7 @@
{
"elapsed_time": 246.0529060009976,
"elapsed_time": 244.54776988498634,
"num_requests": 200,
"total_num_tokens": 147036,
"requests_per_second": 0.8128333180474167,
"tokens_per_second": 597.5787987620997
"requests_per_second": 0.8178361229548825,
"tokens_per_second": 601.2567608739705
}
benchmarks/benchmark_results_rocm/openai_gpt-oss-20b_tp1_throughput.json
+3 -3
Ver fichero
@@ -1,7 +1,7 @@
{
"elapsed_time": 333.59849170300004,
"elapsed_time": 362.9645123449736,
"num_requests": 200,
"total_num_tokens": 147036,
"requests_per_second": 0.5995230943012126,
"tokens_per_second": 440.75738846836555
"requests_per_second": 0.5510180560294371,
"tokens_per_second": 405.0974544317216
}
benchmarks/find_max_context.py
+87 -10
Ver fichero
@@ -15,18 +15,21 @@ except ImportError:
print("Error: 'transformers' not found. Please install it or run in vLLM environment.")
sys.exit(1)
# Import path handling for scripts/models.py
try:
import sys, os
sys.path.append(str(Path(__file__).parent.parent / "scripts"))
import models
import cluster_manager # Import shared cluster logic
except ImportError:
print("Error: Could not import scripts/models.py.")
print("Error: Could not import scripts/models.py or cluster_manager.py.")
sys.exit(1)
# Import Utils from run_vllm_bench (keep utils shared)
try:
from run_vllm_bench import get_gpu_count, kill_vllm
from run_vllm_bench import kill_vllm
# We do NOT import get_gpu_count because we are overriding it for cluster awareness
except ImportError:
print("Error: Could not import run_vllm_bench.py.")
sys.exit(1)
@@ -65,7 +68,30 @@ CONCURRENCY_STEPS = [1, 4, 8, 16]
def log(msg): print(f"[MAX-CTX] {msg}", flush=True)
def get_gpu_count():
"""
Returns total GPUs.
If Ray Cluster is active, returns TOTAL cluster GPUs (e.g., 2).
Otherwise returns local AMD GPUs.
"""
if cluster_manager.check_ray_status():
# Ideally we'd query Ray for total resources, but for this specific 2-node setup:
# If cluster is up, we assume 2 nodes x 1 GPU = 2 GPUs.
# Constructing a Ray client just to count is slow/complex here.
log("Ray Cluster Detected: Assuming 2 GPUs available.")
return 2
# Local Fallback
try:
res = subprocess.run("rocm-smi --showid", shell=True, text=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE)
if res.returncode == 0:
return res.stdout.count("GPU")
except: pass
return 1
def get_hf_context_limit(model_name, trust_remote=False):
# ... (Keep existing implementation)
try:
cfg = AutoConfig.from_pretrained(model_name, trust_remote_code=trust_remote)
@@ -95,6 +121,7 @@ def get_hf_context_limit(model_name, trust_remote=False):
def get_vllm_server_cmd(model, tp_size, util, max_len, max_seqs):
"""
Constructs the vLLM serve command.
Using Ray Backend if tp_size > 1 (Cluster Mode).
"""
config = MODEL_TABLE[model]
@@ -105,15 +132,45 @@ def get_vllm_server_cmd(model, tp_size, util, max_len, max_seqs):
"--tensor-parallel-size", str(tp_size),
"--max-num-seqs", str(max_seqs),
"--dtype", "auto",
# "--disable-log-stats" # Cleaner output, but user managed without it
# "--disable-log-stats"
]
if config.get("trust_remote"): cmd.append("--trust-remote-code")
if config.get("enforce_eager"): cmd.append("--enforce-eager")
# Add model specific env vars
# Env Setup
env = os.environ.copy()
env.update(config.get("env", {}))
# CLUSTER / RAY LOGIC
# Only if we need more than 1 GPU do we engage the cluster machinery
if tp_size > 1:
log(f"TP={tp_size} > 1: Using Ray Distributed Backend")
cmd.extend(["--distributed-executor-backend", "ray"])
# Inject Cluster Env Vars (similar to start_vllm_cluster.py)
# We need to know Head IP and RDMA Interface
rdma_iface = cluster_manager.get_net_iface()
head_ip = cluster_manager.get_local_ip(rdma_iface) # Assuming we run this ON HEAD
# IMPORTANT: vLLM needs to bind to the Head IP for Ray workers to reach it?
# Or at least we should be explicit.
cmd.extend(["--host", head_ip])
# Update our own process env so verify_context knows where to look?
# No, verify_context runs in THIS process. We need to export it or pass it.
# Simplest is to set it in os.environ for OUR process too, but that might be messy.
# Better: We rely on standard PORT.
env["RAY_EXPERIMENTAL_NOSET_ROCR_VISIBLE_DEVICES"] = "1"
env["VLLM_HOST_IP"] = head_ip
env["NCCL_SOCKET_IFNAME"] = rdma_iface
env["NCCL_IB_GID_INDEX"] = "1"
env["NCCL_IB_DISABLE"] = "0"
env["NCCL_NET_GDR_LEVEL"] = "0"
else:
# Default Localhost bind for single node safety
cmd.extend(["--host", "127.0.0.1"])
if config.get("trust_remote"): cmd.append("--trust-remote-code")
if config.get("enforce_eager"): cmd.append("--enforce-eager")
return cmd, env
@@ -300,7 +357,14 @@ def verify_context(model, context_len):
"""
Sends a request to the server with length ~context_len to verify stability.
"""
url = f"http://{HOST}:{PORT}/v1/completions"
# Use dynamic host if set (by cluster logic), else localhost
# But wait, the env var is set for the SERVER process, not necessarily us?
# Actually, we (the client script) need to know where to send requests.
# If we are on Head, localhost is fine for Head-based server.
# But if we use Ray, vLLM head usually binds to HOST IP.
target_host = os.getenv("VLLM_HOST_IP", "127.0.0.1")
url = f"http://{target_host}:{PORT}/v1/completions"
# We use a simple "A " * N prompt.
# Llama 3 tokenizer: "A" is usually 1 token.
@@ -529,9 +593,22 @@ def main():
continue
config = MODEL_TABLE[model]
valid_tps = [t for t in config["valid_tp"] if t <= gpu_count]
for tp in valid_tps:
# KEY CHANGES:
# We only want to test the MINIMUM required TP.
# If model supports 1 and 2, we ONLY test 1 (local is faster/easier).
# We only test 2 if model VALID_TP *starts* with 2 (or higher).
valid_tps = config.get("valid_tp", [1])
min_tp = min(valid_tps)
if min_tp > gpu_count:
log(f"Skipping {model}: Requires TP={min_tp} but only {gpu_count} GPUs available.")
continue
tps_to_test = [min_tp]
for tp in tps_to_test:
# Track successful seqs for this TP to skip lower utils
# effectively: {seqs_count: max_working_util}
# Since we iterate high-util -> low-util, if we succeeded already for this 'seqs', we skip.
docs/index.html
+21 -9
Ver fichero
@@ -469,6 +469,10 @@
style="font-size: 0.9rem; font-weight: 500; display: flex; align-items: center; gap: 4px; cursor: pointer;">
<input type="checkbox" id="toggleTP2" checked> TP2
</label>
<label
style="font-size: 0.9rem; font-weight: 500; display: flex; align-items: center; gap: 4px; cursor: pointer;">
<input type="checkbox" id="toggleTP2Eth" checked> TP2 (Eth)
</label>
</div>
<!-- Attention Group -->
@@ -544,6 +548,7 @@
activeTab: "Throughput",
showTP1: true,
showTP2: true,
showTP2Eth: true,
showTriton: true,
showRocm: false
};
@@ -615,6 +620,7 @@
// Toggles
$('toggleTP1').addEventListener('change', e => { state.showTP1 = e.target.checked; render(); });
$('toggleTP2').addEventListener('change', e => { state.showTP2 = e.target.checked; render(); });
$('toggleTP2Eth').addEventListener('change', e => { state.showTP2Eth = e.target.checked; render(); });
$('toggleTriton').addEventListener('change', e => { state.showTriton = e.target.checked; render(); });
$('toggleRocm').addEventListener('change', e => { state.showRocm = e.target.checked; render(); });
}
@@ -636,13 +642,17 @@
params: run.params_b || run.name_params_b,
results: {
1: { triton: null, rocm: null },
2: { triton: null, rocm: null }
2: { triton: null, rocm: null },
"2_eth": { triton: null, rocm: null }
}
};
}
const m = testGroups[testName].models[modelName];
const tp = run.tp || 1;
let tp = run.tp || 1;
if (tp === 2 && run.network === "Ethernet") {
tp = "2_eth";
}
if (!m.results[tp]) m.results[tp] = { triton: null, rocm: null };
@@ -749,8 +759,12 @@
if (state.showRocm) cols.push({ id: "tp1_rocm", label: "TP1 ROCm" });
}
if (state.showTP2) {
if (state.showTriton) cols.push({ id: "tp2_triton", label: "TP2 Triton" });
if (state.showRocm) cols.push({ id: "tp2_rocm", label: "TP2 ROCm" });
if (state.showTriton) cols.push({ id: "tp2_triton", label: "TP2 RoCE Triton" });
if (state.showRocm) cols.push({ id: "tp2_rocm", label: "TP2 RoCE ROCm" });
}
if (state.showTP2Eth) {
if (state.showTriton) cols.push({ id: "tp2_eth_triton", label: "TP2 Eth Triton" });
if (state.showRocm) cols.push({ id: "tp2_eth_rocm", label: "TP2 Eth ROCm" });
}
// Thead
@@ -790,11 +804,7 @@
// Data Cells
cols.forEach(c => {
let val = null;
if (c.id === "tp1_triton") val = m.results[1]?.triton;
if (c.id === "tp1_rocm") val = m.results[1]?.rocm;
if (c.id === "tp2_triton") val = m.results[2]?.triton;
if (c.id === "tp2_rocm") val = m.results[2]?.rocm;
let val = getVal(m, c.id);
const bg = c.id.startsWith("tp2") ? 'style="background:#fbfdff;"' : "";
rowHtml += `<td class="col-data" ${bg}>${formatVal(val, unit)}</td>`;
@@ -823,6 +833,8 @@
if (colId === "tp1_rocm") return m.results[1]?.rocm;
if (colId === "tp2_triton") return m.results[2]?.triton;
if (colId === "tp2_rocm") return m.results[2]?.rocm;
if (colId === "tp2_eth_triton") return m.results["2_eth"]?.triton;
if (colId === "tp2_eth_rocm") return m.results["2_eth"]?.rocm;
return null;
}
docs/parse_results.py
+6
Ver fichero
@@ -66,6 +66,11 @@ def parse_logs():
if not tp_match: continue
tp = int(tp_match.group(1))
# Network
network = "RoCE"
if "_eth" in rest:
network = "Ethernet"
# Model Name
if "_" in model_part:
model_display = model_part.replace("_", "/", 1)
@@ -87,6 +92,7 @@ def parse_logs():
"params_b": params_b,
"name_params_b": params_b,
"backend": backend_name, # "Triton" or "ROCm"
"network": network,
"error": False
}
docs/results.json
+505 -171
Ver fichero
@@ -1,131 +1,5 @@
{
"runs": [
{
"model": "meta-llama/Meta-Llama-3.1-8B-Instruct",
"model_clean": "meta-llama/Meta-Llama-3.1-8B-Instruct",
"env": "TP1",
"gpu_config": "single",
"quant": "BF16",
"params_b": 8.0,
"name_params_b": 8.0,
"backend": "Triton",
"error": false,
"test": "Throughput",
"tp": 1,
"tps_mean": 383.3285005130725
},
{
"model": "google/gemma-3-12b-it",
"model_clean": "google/gemma-3-12b-it",
"env": "TP1",
"gpu_config": "single",
"quant": "BF16",
"params_b": 12.0,
"name_params_b": 12.0,
"backend": "Triton",
"error": false,
"test": "Throughput",
"tp": 1,
"tps_mean": 169.75141153501525
},
{
"model": "Qwen/Qwen3-14B-AWQ",
"model_clean": "Qwen/Qwen3-14B-AWQ",
"env": "TP1",
"gpu_config": "single",
"quant": "AWQ",
"params_b": 14.0,
"name_params_b": 14.0,
"backend": "Triton",
"error": false,
"test": "Throughput",
"tp": 1,
"tps_mean": 168.9724830093454
},
{
"model": "openai/gpt-oss-20b",
"model_clean": "openai/gpt-oss-20b",
"env": "TP1",
"gpu_config": "single",
"quant": "BF16",
"params_b": 20.0,
"name_params_b": 20.0,
"backend": "Triton",
"error": false,
"test": "Throughput",
"tp": 1,
"tps_mean": 430.8537270233502
},
{
"model": "openai/gpt-oss-120b",
"model_clean": "openai/gpt-oss-120b",
"env": "TP1",
"gpu_config": "single",
"quant": "BF16",
"params_b": 120.0,
"name_params_b": 120.0,
"backend": "Triton",
"error": false,
"test": "Throughput",
"tp": 1,
"tps_mean": 77.93077982357597
},
{
"model": "zai-org/GLM-4.7-Flash",
"model_clean": "zai-org/GLM-4.7-Flash",
"env": "TP1",
"gpu_config": "single",
"quant": "BF16",
"params_b": null,
"name_params_b": null,
"backend": "Triton",
"error": false,
"test": "Throughput",
"tp": 1,
"tps_mean": 246.02994547299596
},
{
"model": "btbtyler09/Qwen3-Coder-30B-A3B-Instruct-gptq-4bit",
"model_clean": "btbtyler09/Qwen3-Coder-30B-A3B-Instruct-gptq-4bit",
"env": "TP1",
"gpu_config": "single",
"quant": "GPTQ",
"params_b": 30.0,
"name_params_b": 30.0,
"backend": "Triton",
"error": false,
"test": "Throughput",
"tp": 1,
"tps_mean": 227.90361622402403
},
{
"model": "btbtyler09/Qwen3-Coder-30B-A3B-Instruct-gptq-8bit",
"model_clean": "btbtyler09/Qwen3-Coder-30B-A3B-Instruct-gptq-8bit",
"env": "TP1",
"gpu_config": "single",
"quant": "GPTQ",
"params_b": 30.0,
"name_params_b": 30.0,
"backend": "Triton",
"error": false,
"test": "Throughput",
"tp": 1,
"tps_mean": 200.1426829468909
},
{
"model": "dazipe/Qwen3-Next-80B-A3B-Instruct-GPTQ-Int4A16",
"model_clean": "dazipe/Qwen3-Next-80B-A3B-Instruct-GPTQ-Int4A16",
"env": "TP1",
"gpu_config": "single",
"quant": "GPTQ",
"params_b": 80.0,
"name_params_b": 80.0,
"backend": "Triton",
"error": false,
"test": "Throughput",
"tp": 1,
"tps_mean": 132.2599488751683
},
{
"model": "meta-llama/Meta-Llama-3.1-8B-Instruct",
"model_clean": "meta-llama/Meta-Llama-3.1-8B-Instruct",
@@ -135,10 +9,11 @@
"params_b": 8.0,
"name_params_b": 8.0,
"backend": "Triton",
"network": "Ethernet",
"error": false,
"test": "Throughput",
"tp": 2,
"tps_mean": 596.5891209659404
"tps_mean": 613.9321196754427
},
{
"model": "google/gemma-3-12b-it",
@@ -149,10 +24,11 @@
"params_b": 12.0,
"name_params_b": 12.0,
"backend": "Triton",
"network": "Ethernet",
"error": false,
"test": "Throughput",
"tp": 2,
"tps_mean": 294.9472545848014
"tps_mean": 291.5155379231269
},
{
"model": "Qwen/Qwen3-14B-AWQ",
@@ -163,10 +39,11 @@
"params_b": 14.0,
"name_params_b": 14.0,
"backend": "Triton",
"network": "Ethernet",
"error": false,
"test": "Throughput",
"tp": 2,
"tps_mean": 348.0799308087054
"tps_mean": 280.05330212131406
},
{
"model": "openai/gpt-oss-20b",
@@ -177,10 +54,11 @@
"params_b": 20.0,
"name_params_b": 20.0,
"backend": "Triton",
"network": "Ethernet",
"error": false,
"test": "Throughput",
"tp": 2,
"tps_mean": 593.7843034224891
"tps_mean": 602.6345456319963
},
{
"model": "openai/gpt-oss-120b",
@@ -191,10 +69,11 @@
"params_b": 120.0,
"name_params_b": 120.0,
"backend": "Triton",
"network": "Ethernet",
"error": false,
"test": "Throughput",
"tp": 2,
"tps_mean": 112.4781801162827
"tps_mean": 85.2809331488931
},
{
"model": "zai-org/GLM-4.7-Flash",
@@ -205,10 +84,11 @@
"params_b": null,
"name_params_b": null,
"backend": "Triton",
"network": "Ethernet",
"error": false,
"test": "Throughput",
"tp": 2,
"tps_mean": 346.0061963818796
"tps_mean": 300.773560320048
},
{
"model": "btbtyler09/Qwen3-Coder-30B-A3B-Instruct-gptq-4bit",
@@ -219,10 +99,11 @@
"params_b": 30.0,
"name_params_b": 30.0,
"backend": "Triton",
"network": "Ethernet",
"error": false,
"test": "Throughput",
"tp": 2,
"tps_mean": 320.69249844623016
"tps_mean": 321.88057686801585
},
{
"model": "btbtyler09/Qwen3-Coder-30B-A3B-Instruct-gptq-8bit",
@@ -233,10 +114,11 @@
"params_b": 30.0,
"name_params_b": 30.0,
"backend": "Triton",
"network": "Ethernet",
"error": false,
"test": "Throughput",
"tp": 2,
"tps_mean": 274.7000183491961
"tps_mean": 274.46004720922855
},
{
"model": "dazipe/Qwen3-Next-80B-A3B-Instruct-GPTQ-Int4A16",
@@ -247,10 +129,176 @@
"params_b": 80.0,
"name_params_b": 80.0,
"backend": "Triton",
"network": "Ethernet",
"error": false,
"test": "Throughput",
"tp": 2,
"tps_mean": 167.00232766189475
"tps_mean": 182.16229690959702
},
{
"model": "mratsim/MiniMax-M2.5-BF16-INT4-AWQ",
"model_clean": "mratsim/MiniMax-M2.5-BF16-INT4-AWQ",
"env": "TP2",
"gpu_config": "dual",
"quant": "BF16",
"params_b": null,
"name_params_b": null,
"backend": "Triton",
"network": "Ethernet",
"error": false,
"test": "Throughput",
"tp": 2,
"tps_mean": 107.62460878889469
},
{
"model": "meta-llama/Meta-Llama-3.1-8B-Instruct",
"model_clean": "meta-llama/Meta-Llama-3.1-8B-Instruct",
"env": "TP2",
"gpu_config": "dual",
"quant": "BF16",
"params_b": 8.0,
"name_params_b": 8.0,
"backend": "Triton",
"network": "RoCE",
"error": false,
"test": "Throughput",
"tp": 2,
"tps_mean": 590.0492703672895
},
{
"model": "google/gemma-3-12b-it",
"model_clean": "google/gemma-3-12b-it",
"env": "TP2",
"gpu_config": "dual",
"quant": "BF16",
"params_b": 12.0,
"name_params_b": 12.0,
"backend": "Triton",
"network": "RoCE",
"error": false,
"test": "Throughput",
"tp": 2,
"tps_mean": 285.8275921888489
},
{
"model": "Qwen/Qwen3-14B-AWQ",
"model_clean": "Qwen/Qwen3-14B-AWQ",
"env": "TP2",
"gpu_config": "dual",
"quant": "AWQ",
"params_b": 14.0,
"name_params_b": 14.0,
"backend": "Triton",
"network": "RoCE",
"error": false,
"test": "Throughput",
"tp": 2,
"tps_mean": 346.2003835540928
},
{
"model": "openai/gpt-oss-20b",
"model_clean": "openai/gpt-oss-20b",
"env": "TP2",
"gpu_config": "dual",
"quant": "BF16",
"params_b": 20.0,
"name_params_b": 20.0,
"backend": "Triton",
"network": "RoCE",
"error": false,
"test": "Throughput",
"tp": 2,
"tps_mean": 607.216674264294
},
{
"model": "openai/gpt-oss-120b",
"model_clean": "openai/gpt-oss-120b",
"env": "TP2",
"gpu_config": "dual",
"quant": "BF16",
"params_b": 120.0,
"name_params_b": 120.0,
"backend": "Triton",
"network": "RoCE",
"error": false,
"test": "Throughput",
"tp": 2,
"tps_mean": 122.62029501860121
},
{
"model": "zai-org/GLM-4.7-Flash",
"model_clean": "zai-org/GLM-4.7-Flash",
"env": "TP2",
"gpu_config": "dual",
"quant": "BF16",
"params_b": null,
"name_params_b": null,
"backend": "Triton",
"network": "RoCE",
"error": false,
"test": "Throughput",
"tp": 2,
"tps_mean": 366.77052981888835
},
{
"model": "btbtyler09/Qwen3-Coder-30B-A3B-Instruct-gptq-4bit",
"model_clean": "btbtyler09/Qwen3-Coder-30B-A3B-Instruct-gptq-4bit",
"env": "TP2",
"gpu_config": "dual",
"quant": "GPTQ",
"params_b": 30.0,
"name_params_b": 30.0,
"backend": "Triton",
"network": "RoCE",
"error": false,
"test": "Throughput",
"tp": 2,
"tps_mean": 320.0197833991106
},
{
"model": "btbtyler09/Qwen3-Coder-30B-A3B-Instruct-gptq-8bit",
"model_clean": "btbtyler09/Qwen3-Coder-30B-A3B-Instruct-gptq-8bit",
"env": "TP2",
"gpu_config": "dual",
"quant": "GPTQ",
"params_b": 30.0,
"name_params_b": 30.0,
"backend": "Triton",
"network": "RoCE",
"error": false,
"test": "Throughput",
"tp": 2,
"tps_mean": 279.7240042842149
},
{
"model": "dazipe/Qwen3-Next-80B-A3B-Instruct-GPTQ-Int4A16",
"model_clean": "dazipe/Qwen3-Next-80B-A3B-Instruct-GPTQ-Int4A16",
"env": "TP2",
"gpu_config": "dual",
"quant": "GPTQ",
"params_b": 80.0,
"name_params_b": 80.0,
"backend": "Triton",
"network": "RoCE",
"error": false,
"test": "Throughput",
"tp": 2,
"tps_mean": 196.2262690032198
},
{
"model": "mratsim/MiniMax-M2.5-BF16-INT4-AWQ",
"model_clean": "mratsim/MiniMax-M2.5-BF16-INT4-AWQ",
"env": "TP2",
"gpu_config": "dual",
"quant": "BF16",
"params_b": null,
"name_params_b": null,
"backend": "Triton",
"network": "RoCE",
"error": false,
"test": "Throughput",
"tp": 2,
"tps_mean": 98.85048345093716
},
{
"model": "meta-llama/Meta-Llama-3.1-8B-Instruct",
@@ -260,25 +308,12 @@
"quant": "BF16",
"params_b": 8.0,
"name_params_b": 8.0,
"backend": "ROCm",
"backend": "Triton",
"network": "RoCE",
"error": false,
"test": "Throughput",
"tp": 1,
"tps_mean": 452.7925628873698
},
{
"model": "google/gemma-3-12b-it",
"model_clean": "google/gemma-3-12b-it",
"env": "TP1",
"gpu_config": "single",
"quant": "BF16",
"params_b": 12.0,
"name_params_b": 12.0,
"backend": "ROCm",
"error": false,
"test": "Throughput",
"tp": 1,
"tps_mean": 292.4074663029466
"tps_mean": 369.23212230245684
},
{
"model": "Qwen/Qwen3-14B-AWQ",
@@ -288,11 +323,12 @@
"quant": "AWQ",
"params_b": 14.0,
"name_params_b": 14.0,
"backend": "ROCm",
"backend": "Triton",
"network": "RoCE",
"error": false,
"test": "Throughput",
"tp": 1,
"tps_mean": 190.86242019407229
"tps_mean": 159.8857312165796
},
{
"model": "openai/gpt-oss-20b",
@@ -302,11 +338,12 @@
"quant": "BF16",
"params_b": 20.0,
"name_params_b": 20.0,
"backend": "ROCm",
"backend": "Triton",
"network": "RoCE",
"error": false,
"test": "Throughput",
"tp": 1,
"tps_mean": 440.75738846836555
"tps_mean": 411.03557062490586
},
{
"model": "openai/gpt-oss-120b",
@@ -316,11 +353,27 @@
"quant": "BF16",
"params_b": 120.0,
"name_params_b": 120.0,
"backend": "ROCm",
"backend": "Triton",
"network": "RoCE",
"error": false,
"test": "Throughput",
"tp": 1,
"tps_mean": 76.44313314138553
"tps_mean": 75.0407548829671
},
{
"model": "zai-org/GLM-4.7-Flash",
"model_clean": "zai-org/GLM-4.7-Flash",
"env": "TP1",
"gpu_config": "single",
"quant": "BF16",
"params_b": null,
"name_params_b": null,
"backend": "Triton",
"network": "RoCE",
"error": false,
"test": "Throughput",
"tp": 1,
"tps_mean": 239.57478116575834
},
{
"model": "btbtyler09/Qwen3-Coder-30B-A3B-Instruct-gptq-4bit",
@@ -330,11 +383,12 @@
"quant": "GPTQ",
"params_b": 30.0,
"name_params_b": 30.0,
"backend": "ROCm",
"backend": "Triton",
"network": "RoCE",
"error": false,
"test": "Throughput",
"tp": 1,
"tps_mean": 229.9835374194385
"tps_mean": 213.74630950782364
},
{
"model": "btbtyler09/Qwen3-Coder-30B-A3B-Instruct-gptq-8bit",
@@ -344,11 +398,12 @@
"quant": "GPTQ",
"params_b": 30.0,
"name_params_b": 30.0,
"backend": "ROCm",
"backend": "Triton",
"network": "RoCE",
"error": false,
"test": "Throughput",
"tp": 1,
"tps_mean": 203.38751203489863
"tps_mean": 186.03115379827653
},
{
"model": "dazipe/Qwen3-Next-80B-A3B-Instruct-GPTQ-Int4A16",
@@ -358,11 +413,27 @@
"quant": "GPTQ",
"params_b": 80.0,
"name_params_b": 80.0,
"backend": "ROCm",
"backend": "Triton",
"network": "RoCE",
"error": false,
"test": "Throughput",
"tp": 1,
"tps_mean": 135.3839809398758
"tps_mean": 125.65027253668944
},
{
"model": "google/gemma-3-12b-it",
"model_clean": "google/gemma-3-12b-it",
"env": "TP1",
"gpu_config": "single",
"quant": "BF16",
"params_b": 12.0,
"name_params_b": 12.0,
"backend": "Triton",
"network": "RoCE",
"error": false,
"test": "Throughput",
"tp": 1,
"tps_mean": 159.95620436815713
},
{
"model": "meta-llama/Meta-Llama-3.1-8B-Instruct",
@@ -373,10 +444,11 @@
"params_b": 8.0,
"name_params_b": 8.0,
"backend": "ROCm",
"network": "Ethernet",
"error": false,
"test": "Throughput",
"tp": 2,
"tps_mean": 649.02791593759
"tps_mean": 682.4360360233941
},
{
"model": "google/gemma-3-12b-it",
@@ -387,10 +459,11 @@
"params_b": 12.0,
"name_params_b": 12.0,
"backend": "ROCm",
"network": "Ethernet",
"error": false,
"test": "Throughput",
"tp": 2,
"tps_mean": 403.09652364564084
"tps_mean": 398.09474781142933
},
{
"model": "Qwen/Qwen3-14B-AWQ",
@@ -401,10 +474,11 @@
"params_b": 14.0,
"name_params_b": 14.0,
"backend": "ROCm",
"network": "Ethernet",
"error": false,
"test": "Throughput",
"tp": 2,
"tps_mean": 371.4058491591393
"tps_mean": 295.31575874126105
},
{
"model": "openai/gpt-oss-20b",
@@ -415,10 +489,11 @@
"params_b": 20.0,
"name_params_b": 20.0,
"backend": "ROCm",
"network": "Ethernet",
"error": false,
"test": "Throughput",
"tp": 2,
"tps_mean": 597.5787987620997
"tps_mean": 490.93757442090305
},
{
"model": "openai/gpt-oss-120b",
@@ -429,10 +504,11 @@
"params_b": 120.0,
"name_params_b": 120.0,
"backend": "ROCm",
"network": "Ethernet",
"error": false,
"test": "Throughput",
"tp": 2,
"tps_mean": 111.8113988472388
"tps_mean": 86.0910643999307
},
{
"model": "btbtyler09/Qwen3-Coder-30B-A3B-Instruct-gptq-4bit",
@@ -443,10 +519,11 @@
"params_b": 30.0,
"name_params_b": 30.0,
"backend": "ROCm",
"network": "Ethernet",
"error": false,
"test": "Throughput",
"tp": 2,
"tps_mean": 315.906032423287
"tps_mean": 321.6166453306162
},
{
"model": "btbtyler09/Qwen3-Coder-30B-A3B-Instruct-gptq-8bit",
@@ -457,10 +534,11 @@
"params_b": 30.0,
"name_params_b": 30.0,
"backend": "ROCm",
"network": "Ethernet",
"error": false,
"test": "Throughput",
"tp": 2,
"tps_mean": 292.0384117325289
"tps_mean": 283.6309502128471
},
{
"model": "dazipe/Qwen3-Next-80B-A3B-Instruct-GPTQ-Int4A16",
@@ -471,10 +549,266 @@
"params_b": 80.0,
"name_params_b": 80.0,
"backend": "ROCm",
"network": "Ethernet",
"error": false,
"test": "Throughput",
"tp": 2,
"tps_mean": 165.5348293928834
"tps_mean": 182.9186467257061
},
{
"model": "mratsim/MiniMax-M2.5-BF16-INT4-AWQ",
"model_clean": "mratsim/MiniMax-M2.5-BF16-INT4-AWQ",
"env": "TP2",
"gpu_config": "dual",
"quant": "BF16",
"params_b": null,
"name_params_b": null,
"backend": "ROCm",
"network": "Ethernet",
"error": false,
"test": "Throughput",
"tp": 2,
"tps_mean": 98.67941666807306
},
{
"model": "meta-llama/Meta-Llama-3.1-8B-Instruct",
"model_clean": "meta-llama/Meta-Llama-3.1-8B-Instruct",
"env": "TP2",
"gpu_config": "dual",
"quant": "BF16",
"params_b": 8.0,
"name_params_b": 8.0,
"backend": "ROCm",
"network": "RoCE",
"error": false,
"test": "Throughput",
"tp": 2,
"tps_mean": 650.1471716939323
},
{
"model": "google/gemma-3-12b-it",
"model_clean": "google/gemma-3-12b-it",
"env": "TP2",
"gpu_config": "dual",
"quant": "BF16",
"params_b": 12.0,
"name_params_b": 12.0,
"backend": "ROCm",
"network": "RoCE",
"error": false,
"test": "Throughput",
"tp": 2,
"tps_mean": 397.9710386242193
},
{
"model": "Qwen/Qwen3-14B-AWQ",
"model_clean": "Qwen/Qwen3-14B-AWQ",
"env": "TP2",
"gpu_config": "dual",
"quant": "AWQ",
"params_b": 14.0,
"name_params_b": 14.0,
"backend": "ROCm",
"network": "RoCE",
"error": false,
"test": "Throughput",
"tp": 2,
"tps_mean": 367.5704596781314
},
{
"model": "openai/gpt-oss-20b",
"model_clean": "openai/gpt-oss-20b",
"env": "TP2",
"gpu_config": "dual",
"quant": "BF16",
"params_b": 20.0,
"name_params_b": 20.0,
"backend": "ROCm",
"network": "RoCE",
"error": false,
"test": "Throughput",
"tp": 2,
"tps_mean": 601.2567608739705
},
{
"model": "openai/gpt-oss-120b",
"model_clean": "openai/gpt-oss-120b",
"env": "TP2",
"gpu_config": "dual",
"quant": "BF16",
"params_b": 120.0,
"name_params_b": 120.0,
"backend": "ROCm",
"network": "RoCE",
"error": false,
"test": "Throughput",
"tp": 2,
"tps_mean": 118.34229353876268
},
{
"model": "btbtyler09/Qwen3-Coder-30B-A3B-Instruct-gptq-4bit",
"model_clean": "btbtyler09/Qwen3-Coder-30B-A3B-Instruct-gptq-4bit",
"env": "TP2",
"gpu_config": "dual",
"quant": "GPTQ",
"params_b": 30.0,
"name_params_b": 30.0,
"backend": "ROCm",
"network": "RoCE",
"error": false,
"test": "Throughput",
"tp": 2,
"tps_mean": 333.147212194374
},
{
"model": "btbtyler09/Qwen3-Coder-30B-A3B-Instruct-gptq-8bit",
"model_clean": "btbtyler09/Qwen3-Coder-30B-A3B-Instruct-gptq-8bit",
"env": "TP2",
"gpu_config": "dual",
"quant": "GPTQ",
"params_b": 30.0,
"name_params_b": 30.0,
"backend": "ROCm",
"network": "RoCE",
"error": false,
"test": "Throughput",
"tp": 2,
"tps_mean": 295.0301359026215
},
{
"model": "dazipe/Qwen3-Next-80B-A3B-Instruct-GPTQ-Int4A16",
"model_clean": "dazipe/Qwen3-Next-80B-A3B-Instruct-GPTQ-Int4A16",
"env": "TP2",
"gpu_config": "dual",
"quant": "GPTQ",
"params_b": 80.0,
"name_params_b": 80.0,
"backend": "ROCm",
"network": "RoCE",
"error": false,
"test": "Throughput",
"tp": 2,
"tps_mean": 193.87438091607942
},
{
"model": "meta-llama/Meta-Llama-3.1-8B-Instruct",
"model_clean": "meta-llama/Meta-Llama-3.1-8B-Instruct",
"env": "TP1",
"gpu_config": "single",
"quant": "BF16",
"params_b": 8.0,
"name_params_b": 8.0,
"backend": "ROCm",
"network": "RoCE",
"error": false,
"test": "Throughput",
"tp": 1,
"tps_mean": 433.5736973626181
},
{
"model": "Qwen/Qwen3-14B-AWQ",
"model_clean": "Qwen/Qwen3-14B-AWQ",
"env": "TP1",
"gpu_config": "single",
"quant": "AWQ",
"params_b": 14.0,
"name_params_b": 14.0,
"backend": "ROCm",
"network": "RoCE",
"error": false,
"test": "Throughput",
"tp": 1,
"tps_mean": 180.43566315423652
},
{
"model": "openai/gpt-oss-20b",
"model_clean": "openai/gpt-oss-20b",
"env": "TP1",
"gpu_config": "single",
"quant": "BF16",
"params_b": 20.0,
"name_params_b": 20.0,
"backend": "ROCm",
"network": "RoCE",
"error": false,
"test": "Throughput",
"tp": 1,
"tps_mean": 405.0974544317216
},
{
"model": "openai/gpt-oss-120b",
"model_clean": "openai/gpt-oss-120b",
"env": "TP1",
"gpu_config": "single",
"quant": "BF16",
"params_b": 120.0,
"name_params_b": 120.0,
"backend": "ROCm",
"network": "RoCE",
"error": false,
"test": "Throughput",
"tp": 1,
"tps_mean": 74.75385852312364
},
{
"model": "btbtyler09/Qwen3-Coder-30B-A3B-Instruct-gptq-4bit",
"model_clean": "btbtyler09/Qwen3-Coder-30B-A3B-Instruct-gptq-4bit",
"env": "TP1",
"gpu_config": "single",
"quant": "GPTQ",
"params_b": 30.0,
"name_params_b": 30.0,
"backend": "ROCm",
"network": "RoCE",
"error": false,
"test": "Throughput",
"tp": 1,
"tps_mean": 214.65152188564062
},
{
"model": "btbtyler09/Qwen3-Coder-30B-A3B-Instruct-gptq-8bit",
"model_clean": "btbtyler09/Qwen3-Coder-30B-A3B-Instruct-gptq-8bit",
"env": "TP1",
"gpu_config": "single",
"quant": "GPTQ",
"params_b": 30.0,
"name_params_b": 30.0,
"backend": "ROCm",
"network": "RoCE",
"error": false,
"test": "Throughput",
"tp": 1,
"tps_mean": 188.17083503449163
},
{
"model": "dazipe/Qwen3-Next-80B-A3B-Instruct-GPTQ-Int4A16",
"model_clean": "dazipe/Qwen3-Next-80B-A3B-Instruct-GPTQ-Int4A16",
"env": "TP1",
"gpu_config": "single",
"quant": "GPTQ",
"params_b": 80.0,
"name_params_b": 80.0,
"backend": "ROCm",
"network": "RoCE",
"error": false,
"test": "Throughput",
"tp": 1,
"tps_mean": 128.30078036872973
},
{
"model": "google/gemma-3-12b-it",
"model_clean": "google/gemma-3-12b-it",
"env": "TP1",
"gpu_config": "single",
"quant": "BF16",
"params_b": 12.0,
"name_params_b": 12.0,
"backend": "ROCm",
"network": "RoCE",
"error": false,
"test": "Throughput",
"tp": 1,
"tps_mean": 267.99881204205957
}
]
}
scripts/start_vllm.py
+25 -17
Ver fichero
@@ -36,6 +36,22 @@ else:
HOST = os.getenv("HOST", "0.0.0.0")
PORT = os.getenv("PORT", "8000")
def detect_gpus():
"""Detects AMD GPUs via rocm-smi or /dev/dri."""
try:
# Try rocm-smi first
res = subprocess.run(["rocm-smi", "--showid", "--csv"], stdout=subprocess.PIPE, stderr=subprocess.PIPE, text=True)
if res.returncode == 0:
count = res.stdout.count("GPU")
if count > 0: return count
except: pass
# Fallback to /dev/dri/render*
try:
return len(list(Path("/dev/dri").glob("renderD*")))
except:
return 1
def get_discovered_models():
"""
Overrides the hardcoded MODELS_TO_RUN by looking at what we actually have results for.
@@ -93,22 +109,6 @@ def check_dependencies():
print("Error: 'dialog' is required. Please install it (apt-get install dialog).")
sys.exit(1)
def detect_gpus():
"""Detects AMD GPUs via rocm-smi or /dev/dri."""
try:
# Try rocm-smi first
res = subprocess.run(["rocm-smi", "--showid", "--csv"], stdout=subprocess.PIPE, stderr=subprocess.PIPE, text=True)
if res.returncode == 0:
count = res.stdout.count("GPU")
if count > 0: return count
except: pass
# Fallback to /dev/dri/render*
try:
return len(list(Path("/dev/dri").glob("renderD*")))
except:
return 1
def get_verified_config(model_id, tp_size, max_seqs):
"""
Reads max_context_results.json to find the best verified configuration.
@@ -334,7 +334,15 @@ def configure_and_launch(model_idx, gpu_count):
print(f" Backend: {'ROCm' if use_rocm_attn else 'Triton'}")
if clear_cache:
print(f" Action: Clearing vLLM Cache (~/.cache/vllm)")
print(f" Command: {' '.join(cmd)}")
# Variables that represent the custom environment overrides for models
custom_env = config.get("env", {})
if custom_env:
print("\n --- Environment Variables ---")
for k, v in custom_env.items():
print(f" export {k}={v}")
print(f"\n Command: {' '.join(cmd)}")
print("="*60 + "\n")
os.execvpe("vllm", cmd, env)