Can Qwen3.5 122B A10B run on H100 NVL 188GB?

YES — Runs Great

C53Usable

Estimated from fit model

Qwen3.5 122B A10B needs ~94.1 GB VRAM. H100 NVL 188GB has 188.0 GB. With Q3_K_M quantization, expect ~98 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

F16 (Maximum quality) — 284.4 GB, exceeds 188.0 GB available

284.4 GB required188.0 GB available

151% VRAM needed

96.4 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

15.2 tok/s

TTFT

12706 ms

Safe context

Memory

284.4 GB / 188.0 GB

Offload

30%

Memory breakdown

Weights250.1 GB

KV Cache14.3 GB

Runtime1.2 GB

Headroom18.8 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsQwen3.5 122B A10B 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: 15.2 tok/s decode · 12.7s TTFT (warm) · 38 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	98.3 tok/s	1074 ms	121K
Coding	C	Runs well	98.3 tok/s	1970 ms	121K
Agentic Coding	C	Runs well	98.3 tok/s	2865 ms	121K
Reasoning	C	Runs well	98.3 tok/s	2328 ms	121K
RAG	C	Runs well	98.3 tok/s	3581 ms	121K

Quantization options

How Qwen3.5 122B A10B (122B params) fits at each quantization level on H100 NVL 188GB (188.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	47.6 GB	Low	C42
Q3_K_S	3	59.8 GB	Low	C43
NVFP4	4	68.3 GB	Medium	C44
Q4_K_M	4	74.4 GB	Medium	C45
Q5_K_M	5	87.8 GB	High	C46
Q6_K	6	100.0 GB	High	C48
Q8_0Best for your GPU	8	130.5 GB	Very High	C48
F16	16	250.1 GB	Maximum	F0

Get started

Copy-paste commands to run Qwen3.5 122B A10B on your machine.

Run

docker run --rm -it ghcr.io/ggerganov/llama.cpp:full \
  --hf-repo "unsloth/Qwen3.5-122B-A10B-GGUF" \
  --hf-file "Qwen3.5-122B-A10B-GGUF-Q3_K_M.gguf" \
  -c 4096 -ngl 99

Frequently asked questions

Can H100 NVL 188GB run Qwen3.5 122B A10B?

Yes, H100 NVL 188GB can run Qwen3.5 122B A10B with a C grade (Runs well). Expected decode speed: 98.3 tok/s.

How much VRAM does Qwen3.5 122B A10B need?

Qwen3.5 122B A10B (122B parameters) requires approximately 94.1 GB of memory with Q3_K_M quantization.

What is the best quantization for Qwen3.5 122B A10B?

The recommended quantization for Qwen3.5 122B A10B is Q3_K_M, which balances quality and memory efficiency.

What speed will Qwen3.5 122B A10B run at on H100 NVL 188GB?

On H100 NVL 188GB, Qwen3.5 122B A10B achieves approximately 98.3 tokens per second decode speed with a time-to-first-token of 1970ms using Q3_K_M quantization.

Can H100 NVL 188GB run Qwen3.5 122B A10B for coding?

For coding workloads, Qwen3.5 122B A10B on H100 NVL 188GB receives a C grade with 98.3 tok/s and 121K context.

What context window can Qwen3.5 122B A10B use on H100 NVL 188GB?

On H100 NVL 188GB, Qwen3.5 122B A10B can safely use up to 121K 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 122B A10B

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