Mixtral 8x22B H100 vs A100 throughput is the first thing you should benchmark before committing to a serving stack for this 176B-parameter sparse mixture-of-experts model. The two GPUs share the same 80GB memory ceiling, but everything underneath—bandwidth, compute, and numeric format support—changes the math for token generation under load.
Hardware baseline that actually matters
The A100 80GB ships HBM2e at 2.0 TB/s and peaks at 312 TFLOPS in FP16. The H100 SXM keeps the 80GB capacity but moves to HBM3 at 3.35 TB/s, hitting 989 TFLOPS FP16 and 1979 TFLOPS in FP8. NVLink bandwidth also jumps from 600 GB/s to 900 GB/s per GPU. For Mixtral 8x22B, which has 8 experts of 22B parameters each (≈39B active per token), weight footprint dominates: full FP16 weights need ~352 GB, so neither card holds the model alone. You are always in a tensor-parallel world.
Mixtral 8x22B inference profile
Mixtral 8x22B is expert-parallel across 8 shards with 2 active per forward pass. The inactive experts still occupy memory; only the active set computes. That makes the model memory-capacity bound at load time and memory-bandwidth bound at decode time. A100’s 2.0 TB/s forces more GPUs to hit the same tokens/sec target. H100’s extra bandwidth and FP8 path let you shrink the numeric footprint or push larger batches through the same node count.
A minimal vLLM launch for a 2-GPU H100 node looks like:
python -m vllm.entrypoints.openai.api_server \
--model mistralai/Mixtral-8x22B-Instruct-v0.1 \
--tensor-parallel-size 2 \
--dtype auto \
--max-model-len 32768On A100 you typically need --tensor-parallel-size 4 for the same FP16 model to fit comfortably with KV cache headroom.
Latency and throughput under batching
Under continuous batching with vLLM or TRT-LLM, H100 delivers proportionally higher decode throughput because each token pull fetches expert weights through a wider pipe. At small batch (1–8), the gap is modest—maybe 1.4–1.8x—because compute isn’t saturated. At large batch (64+), H100 opens to 2–3x the tokens/sec of A100, assuming FP8 kernels are engaged. FP8 on H100 halves weight reads versus FP16, directly cutting the decode bottleneck.
If you front inference with a gateway such as n4n.ai, you can issue client routing directives to prefer H100 backends for Mixtral 8x22B when throughput matters, while still getting automatic fallback to A100 during capacity crunches.
A client call with routing preference might look like:
from openai import OpenAI
client = OpenAI(
base_url="https://api.n4n.ai/v1",
api_key="sk-...",
default_headers={"x-n4n-routing": '{"gpu_class":"h100"}'}
)
resp = client.chat.completions.create(
model="mistralai/Mixtral-8x22B-Instruct-v0.1",
messages=[{"role":"user","content":"Summarize this RFC."}]
)Cost model and TCO
H100 instances carry a 2–3x price premium over A100 on every major cloud. But because a single H100 node replaces ~2 A100 nodes for the same Mixtral 8x22B throughput target, the per-token cost often lands comparable or cheaper at scale. At low utilization, A100 is cheaper because you’re not paying for idle H100 silicon. Per-token metering (as provided by some gateways) makes this visible: watch the completion_tokens vs cost ratio when you shift traffic.
Ergonomics and software
A100 runs on CUDA 11.x and is rock-solid with older vLLM and TGI releases. H100 wants CUDA 12 and recent drivers; FP8 requires Transformer Engine or vLLM’s experimental FP8 path. If your team ships containers monthly, A100 is lower friction. H100 needs you to actually use the new datatypes—running FP16 on H100 leaves most of the silicon dark.
Ecosystem and tooling
Both GPUs are first-class in vLLM, TensorRT-LLM, and Hugging Face TGI. H100 gets early FP8 and CUDA graph optimizations; A100 waits for backports. For Mixtral specifically, expert parallelism is supported in TRT-LLM’s MoE kernels on both, but H100’s higher NVLink reduces all-reduce stalls when TP size grows.
Limits and caveats
A100’s hard limit is memory bandwidth: once you exceed batch 32, latency climbs fast. H100’s limit is supply and software maturity—FP8 MoE kernels are still shifting between releases. Neither card handles FP16 Mixtral 8x22B in a single GPU; plan for multi-GPU orchestration regardless.
Head-to-head comparison
| Dimension | A100 80GB | H100 80GB |
|---|---|---|
| Capabilities | FP16/BF16, HBM2e, NVLink 600GB/s | FP8/FP16/BF16, HBM3, NVLink 900GB/s, Transformer Engine |
| Price/cost model | Lower hourly rate, needs 2x nodes for same throughput | 2–3x hourly rate, fewer nodes needed at scale |
| Latency/throughput | 2.0 TB/s bandwidth; 1.4–1.8x at low batch, falls behind at high batch | 3.35 TB/s; 2–3x tokens/sec at large batch with FP8 |
| Ergonomics | CUDA 11, mature tooling, drop-in | CUDA 12 required, FP8 needs newer stacks |
| Ecosystem | Universal vLLM/TGI/TRT-LLM support | Same support, FP8 early-access kernels |
| Limits | Bandwidth-bound beyond TP=4 | Supply constraints, kernel churn |
Which to choose
Prototype and low-QPS dev: Use A100. The lower cost and mature software let you iterate on prompts and batch logic without fighting FP8 paths. A single 4-GPU A100 box serves dev traffic fine.
High-throughput production (chat, RAG): Pick H100 with FP8. The 2–3x decode gain at batch 64+ directly cuts tail latency and node count. If your gateway honors routing hints, pin H100 for this model.
Cost-sensitive batch jobs: A100 still wins when you can tolerate slower jobs and run on spot. Schedule bulk summarization on A100 fleets; keep H100 for interactive paths.
Latency-critical single-stream: Neither is ideal alone; use H100 with small TP and CUDA graphs to minimize per-token delay. A100 will show 30–50% higher time-to-first-token at identical config.
Pick based on batch shape, not hype. Mixtral 8x22B H100 vs A100 throughput diverges only when you push concurrent requests—bench your own p50/p99 before buying.