Can Qwen3.5 35B A3B run on H100 NVL 188GB?

YES — Runs Great

C47Usable

Estimated from fit model

Qwen3.5 35B A3B needs ~45.5 GB VRAM. H100 NVL 188GB has 188.0 GB. With Q4_K_M quantization, expect ~296 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: HighStack: BasicBottleneck: Balanced

Operating mode

Choose the run profile you care about

Interactive favors responsiveness, while light API and scale-out lean harder on serving readiness. The fit stays the same, but the recommendation lens changes.

Current mode

Balanced

Balanced for general local use. Keeps the ranking neutral across personal and serving workflows.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 45.5 GB, 295.9 tok/s, Runs well

45.5 GB required188.0 GB available

24% VRAM used

Fit status

Runs well

Decode

295.9 tok/s

TTFT

654 ms

Safe context

572K

Memory

45.5 GB / 188.0 GB

Memory breakdown

Weights21.3 GB

KV Cache4.1 GB

Runtime1.2 GB

Headroom18.8 GB

See how fast it feels

See how fast it feelsQwen3.5 35B A3B on H100 NVL 188GB
1st promptCold start — includes initialization
>What is local AI inference?
Local AI inference means running an AI language model directly on your own hardware — your laptop, desktop, or server — instead of sending requests to a remote cloud API.

When you run inference locally the model weights are loaded into your GPU or unified memory. Each token you generate requires reading those weights from memory, so memory bandwidth is the main bottleneck for decode speed.

Key benefits of running locally:
  - Full privacy: your prompts never leave your machine
  - No per-token cost or rate limits
  - Works offline once the model is downloaded
  - Latency depends only on your hardware
2nd promptWarm — model ready, no init overhead
>How much VRAM do I need?
It depends on the model size and quantization level. A rough rule of thumb:

  Model size     Q4 (4-bit)    Q8 (8-bit)    FP16
  7B params      ~4.3 GB       ~7.5 GB       ~14 GB
  13B params     ~7.9 GB       ~13.9 GB      ~26 GB
  70B params     ~42.7 GB      ~74.9 GB      ~140 GB

Most people use 4-bit quantization (Q4_K_M) which gives 90-95% of full quality at a fraction of the memory. A 24 GB GPU can comfortably run most 7B-13B models.
Estimated: 295.9 tok/s decode · 654ms TTFT (warm) · 740 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

No major red flags

This recommendation has enough memory headroom and acceptable estimated speed for the selected workload.

Best improvement path

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	C	Runs well	295.9 tok/s	357 ms	572K
Coding	C	Runs well	295.9 tok/s	654 ms	572K
Agentic Coding	C	Runs well	295.9 tok/s	952 ms	572K
Reasoning	C	Runs well	295.9 tok/s	773 ms	572K
RAG	C	Runs well	295.9 tok/s	1189 ms	572K

Quantization options

How Qwen3.5 35B A3B (35B params) fits at each quantization level on H100 NVL 188GB (188.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	13.7 GB	Low	D38
Q3_K_S	3	17.2 GB	Low	D38
NVFP4	4	19.6 GB	Medium	D38
Q4_K_M	4	21.3 GB	Medium	D38
Q5_K_M	5	25.2 GB	High	D39
Q6_K	6	28.7 GB	High	D39
Q8_0	8	37.5 GB	Very High	D40
F16Best for your GPU	16	71.8 GB	Maximum	C44

Get started

Copy-paste commands to run Qwen3.5 35B A3B on your machine.

Run

docker run --rm -it ghcr.io/ggerganov/llama.cpp:full \
  --hf-repo "unsloth/Qwen3.5-35B-A3B-GGUF" \
  --hf-file "Qwen3.5-35B-A3B-GGUF-Q4_K_M.gguf" \
  -c 4096 -ngl 99

Frequently asked questions

Can H100 NVL 188GB run Qwen3.5 35B A3B?

Yes, H100 NVL 188GB can run Qwen3.5 35B A3B with a C grade (Runs well). Expected decode speed: 295.9 tok/s.

How much VRAM does Qwen3.5 35B A3B need?

Qwen3.5 35B A3B (35B parameters) requires approximately 45.5 GB of memory with Q4_K_M quantization.

What is the best quantization for Qwen3.5 35B A3B?

The recommended quantization for Qwen3.5 35B A3B is Q4_K_M, which balances quality and memory efficiency.

What speed will Qwen3.5 35B A3B run at on H100 NVL 188GB?

On H100 NVL 188GB, Qwen3.5 35B A3B achieves approximately 295.9 tokens per second decode speed with a time-to-first-token of 654ms using Q4_K_M quantization.

Can H100 NVL 188GB run Qwen3.5 35B A3B for coding?

For coding workloads, Qwen3.5 35B A3B on H100 NVL 188GB receives a C grade with 295.9 tok/s and 572K context.

What context window can Qwen3.5 35B A3B use on H100 NVL 188GB?

On H100 NVL 188GB, Qwen3.5 35B A3B can safely use up to 572K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for H100 NVL 188GB See all hardware for Qwen3.5 35B A3B

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