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B200 inference benchmarks for Llama 3.3 70B

A practical analysis of B200 Llama 3.3 70B inference benchmark results: real throughput, latency tradeoffs, and when Blackwell beats Hopper.

n4n Team4 min read901 words

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The B200 Llama 3.3 70B inference benchmark data coming out of early Blackwell deployments tells a nuanced story: the GPU wins on cost-per-token at scale, not on single-request latency. The thesis here is simple—you only see dramatic gains from B200 when you run Llama 3.3 70B in FP8 with large batches; run it naively in FP16 at low concurrency and you’ve bought an expensive space heater.

Why Llama 3.3 70B is the right stress test

Llama 3.3 70B is a dense transformer with ~70 billion parameters. In FP16, weights consume 140 GB, which exceeds the 80 GB of an H100/H200 and forces tensor parallelism across two GPUs. In FP8, weights drop to 70 GB, fitting a single B200’s 192 GB HBM3e comfortably with headroom for KV cache. That makes it a perfect probe for Blackwell’s mixed-precision throughput claims.

A B200 Llama 3.3 70B inference benchmark also exposes software maturity gaps. The model is new enough that reference kernels lag behind H100-optimized paths.

Hardware reality: what B200 actually provides

B200 ships 192 GB HBM3e at 8 TB/s bandwidth. NVIDIA positions its FP8 throughput at roughly double H100’s FP8 sparse rating, with FP4 options absent on Hopper. The memory bandwidth increase from 3.35 TB/s (H100) to 8 TB/s is the bigger lever for inference, since decoder steps are bandwidth-bound.

For a 70B model in FP8, a single B200 holds weights and serves batches of modest size without NVLink hops. Two B200s in a node give you 384 GB and 16 TB/s aggregate—overkill for FP8 weights but useful for long-context KV caches.

Reproducing a meaningful benchmark

Don’t trust a single-number tweet. Run a shaped load. Below is a minimal vLLM launch for FP8 on a single B200:

docker run --gpus '"device=0"' \
  -p 8000:8000 \
  vllm/vllm:latest \
  --model meta-llama/Llama-3.3-70B-Instruct \
  --quantization fp8 \
  --tensor-parallel-size 1 \
  --max-model-len 8192 \
  --gpu-memory-utilization 0.9

Then drive it with a client that varies concurrency:

import asyncio, openai, time

client = openai.AsyncOpenAI(base_url="http://localhost:8000/v1", api_key="EMPTY")

async def req(n):
    t0 = time.monotonic()
    await client.chat.completions.create(
        model="meta-llama/Llama-3.3-70B-Instruct",
        messages=[{"role":"user","content":"Explain TCP fast open in 200 words."}],
        max_tokens=200)
    return time.monotonic()-t0

async def sweep(concurrency):
    tasks = [req(i) for i in range(concurrency)]
    return await asyncio.gather(*tasks)

for c in [1, 8, 32, 64, 128]:
    lat = asyncio.run(sweep(c))
    print(f"concurrency={c} p50={sorted(lat)[len(lat)//2]:.3f}s")

A proper B200 Llama 3.3 70B inference benchmark records tokens/sec per watt, not just p50 latency.

Latency at the edge: single-stream cases

For one concurrent request, the B200’s extra bandwidth barely matters. The decode step moves weights once and streams KV cache. On H100, p50 time-to-first-token for a 2k prompt might be ~40 ms; on B200, expect a marginal drop because compute isn’t the bottleneck. If your product is a chatbot with one user, the B200 Llama 3.3 70B inference benchmark difference is within noise.

Where B200 helps single-stream is long context: with 8k+ input, the prefill phase is memory-bound. Higher bandwidth cuts prefill latency by roughly the bandwidth ratio (≈2.4x theoretical), but real kernels rarely hit peak.

Saturated throughput: where Blackwell pays off

Push concurrency to 64+. The FP8 B200 serves more tokens per second because it moves 70 GB weights through 8 TB/s instead of H100’s 3.35 TB/s, and the tensor cores chew FP8 matmuls faster. Published NVIDIA figures suggest ~2x H100 FP8 throughput per GPU; independent labs echo 1.6–1.9x on 70B models with tuned kernels.

A B200 Llama 3.3 70B inference benchmark at batch 128 might show 3–4x the tokens/sec of a single H100 running FP16 (because the H100 needs two GPUs for the weights, adding communication tax). That’s the real story: B200 collapses a 2-GPU serving node into one.

Quantization is not free

FP8 weight-only quant on Llama 3.3 70B loses <1% on MMLU-style evals, but activation outliers can bite. Use calibrated FP8 (e.g., QuIP or native vLLM fp8) not naive cast. If you drop to INT4, you fit two models per B200, but perplexity degradation demands careful evaluation.

{
  "quantization": "fp8",
  "calibration": "act_scaling",
  "risk": "hidden_state_outliers"
}

Skip quantization and the B200 runs FP16 at similar efficiency to H100 per FLOP, wasting its memory advantage.

Software gaps you will hit

Blackwell’s compute capabilities are ahead of open-source kernels. As of early 2025, vLLM FP8 path on B200 is stable but lacks some fused attention variants and paged KV cache reuse across FP8 is young. TensorRT-LLM extracts more, but requires rebuild per model.

If your stack assumes H100 CUDA graphs, test them on B200—graph capture memory is higher.

Power and total cost

B200 draws ~1000 W per GPU vs H100’s 700 W. The throughput per watt at FP8 still favors B200 because you replace two H100s (1400 W) with one B200 for the same FP8 model. But if you run FP16, you still need two B200s for 70B, and power goes up.

Datacenter caps matter. A rack of B200s needs 3x the PSU headroom of H100 generation.

Routing across silicon generations

In a heterogeneous fleet, you want requests to land on the cheapest GPU that meets latency SLO. An inference gateway that honors client routing directives can pin batch jobs to B200 and interactive traffic to H100. n4n.ai forwards provider cache-control hints and falls back automatically when a B200 pool is degraded, which keeps p99 stable without manual failover code.

{
  "route": {"prefer": ["b200-fp8"], "fallback": ["h100-fp8"]},
  "cache_control": {"type": "ephemeral"}
}

Tradeoff summary

  • Single-user latency: B200 ≈ H100, maybe 10–20% better on long prefill.
  • High-batch cost/token: B200 wins by 1.5–2x over H100 FP8, more vs H100 FP16.
  • Software risk: FP8 kernels mature but younger than Hopper.
  • Power: Higher per GPU, lower per token at FP8.

Takeaway

Buy B200 for Llama 3.3 70B only if you will serve it in FP8 at concurrency > 32 and you can tolerate early-adopter kernel quirks. The B200 Llama 3.3 70B inference benchmark proves Blackwell’s value is economic, not magical: it turns a two-GPU problem into a one-GPU problem and doubles throughput per watt. If your traffic is sparse or you refuse to quantize, stay on H100/H200 and wait for GA-quality drivers.

Tagsb200llama-3-3gpu-benchmarkinference-latency

Written by

n4n Team

The team building n4n — a single OpenAI-compatible API in front of 240+ models, with automatic fallback, load balancing and pay-per-token metering.

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