Can Qwen3.5 35B A3B run on RTX 6000 Ada 48GB?

YES — Runs Great

C53Usable

Estimated from fit model

Qwen3.5 35B A3B needs ~31.5 GB VRAM. RTX 6000 Ada 48GB has 48.0 GB. With Q4_K_M quantization, expect ~37 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) — 31.5 GB, 36.9 tok/s, Runs well

31.5 GB required48.0 GB available

66% VRAM used

Fit status

Runs well

Decode

36.9 tok/s

TTFT

5251 ms

Safe context

81K

Memory

31.5 GB / 48.0 GB

Memory breakdown

Weights21.3 GB

KV Cache4.1 GB

Runtime1.2 GB

Headroom4.8 GB

See how fast it feels

See how fast it feelsQwen3.5 35B A3B on RTX 6000 Ada 48GB
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: 36.9 tok/s decode · 5.3s TTFT (warm) · 92 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	36.9 tok/s	2864 ms	81K
Coding	C	Runs well	36.9 tok/s	5251 ms	81K
Agentic Coding	C	Runs well	36.9 tok/s	7637 ms	81K
Reasoning	C	Runs well	36.9 tok/s	6205 ms	81K
RAG	C	Runs well	36.9 tok/s	9547 ms	81K

Quantization options

How Qwen3.5 35B A3B (35B params) fits at each quantization level on RTX 6000 Ada 48GB (48.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	13.7 GB	Low	C44
Q3_K_S	3	17.2 GB	Low	C46
NVFP4	4

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 "lmstudio-community/Qwen3.5-35B-A3B-GGUF" \
  --hf-file "Qwen3.5-35B-A3B-GGUF-Q4_K_M.gguf" \
  -c 4096 -ngl 99

Frequently asked questions

Can RTX 6000 Ada 48GB run Qwen3.5 35B A3B?

Yes, RTX 6000 Ada 48GB can run Qwen3.5 35B A3B with a C grade (Runs well). Expected decode speed: 36.9 tok/s.

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

Qwen3.5 35B A3B (35B parameters) requires approximately 31.5 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 RTX 6000 Ada 48GB?

On RTX 6000 Ada 48GB, Qwen3.5 35B A3B achieves approximately 36.9 tokens per second decode speed with a time-to-first-token of 5251ms using Q4_K_M quantization.

Can RTX 6000 Ada 48GB run Qwen3.5 35B A3B for coding?

For coding workloads, Qwen3.5 35B A3B on RTX 6000 Ada 48GB receives a C grade with 36.9 tok/s and 81K context.

What context window can Qwen3.5 35B A3B use on RTX 6000 Ada 48GB?

On RTX 6000 Ada 48GB, Qwen3.5 35B A3B can safely use up to 81K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for RTX 6000 Ada 48GB See all hardware for Qwen3.5 35B A3B

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