Can Qwen3.5 27B run on RTX 6000 Ada 48GB?

YES — Runs Great

C52Usable

Estimated from fit model

Qwen3.5 27B needs ~25.6 GB VRAM. RTX 6000 Ada 48GB has 48.0 GB. With Q4_K_M quantization, expect ~48 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) — 25.6 GB, 47.8 tok/s, Runs well

25.6 GB required48.0 GB available

53% VRAM used

Fit status

Runs well

Decode

47.8 tok/s

TTFT

4051 ms

Safe context

129K

Memory

25.6 GB / 48.0 GB

Memory breakdown

Weights16.5 GB

KV Cache3.2 GB

Runtime1.2 GB

Headroom4.8 GB

See how fast it feels

See how fast it feelsQwen3.5 27B 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: 47.8 tok/s decode · 4.1s TTFT (warm) · 120 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	47.8 tok/s	2209 ms	129K
Coding	C	Runs well	47.8 tok/s	4051 ms	129K
Agentic Coding	C	Runs well	47.8 tok/s	5892 ms	129K
Reasoning	C	Runs well	47.8 tok/s	4787 ms	129K
RAG	C	Runs well	47.8 tok/s	7365 ms	129K

Quantization options

How Qwen3.5 27B (27B params) fits at each quantization level on RTX 6000 Ada 48GB (48.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	10.5 GB	Low	C44
Q3_K_S	3	13.2 GB	Low	C44
NVFP4	4	15.1 GB	Medium	C45
Q4_K_M	4	16.5 GB	Medium	C45
Q5_K_M	5	19.4 GB	High	C46
Q6_K	6	22.1 GB	High	C47
Q8_0Best for your GPU	8	28.9 GB	Very High	C48
F16	16	55.4 GB	Maximum	F0

Get started

Copy-paste commands to run Qwen3.5 27B on your machine.

Run

docker run --rm -it ghcr.io/ggerganov/llama.cpp:full \
  --hf-repo "unsloth/Qwen3.5-27B-GGUF" \
  --hf-file "Qwen3.5-27B-GGUF-Q4_K_M.gguf" \
  -c 4096 -ngl 99

Frequently asked questions

Can RTX 6000 Ada 48GB run Qwen3.5 27B?

Yes, RTX 6000 Ada 48GB can run Qwen3.5 27B with a C grade (Runs well). Expected decode speed: 47.8 tok/s.

How much VRAM does Qwen3.5 27B need?

Qwen3.5 27B (27B parameters) requires approximately 25.6 GB of memory with Q4_K_M quantization.

What is the best quantization for Qwen3.5 27B?

The recommended quantization for Qwen3.5 27B is Q4_K_M, which balances quality and memory efficiency.

What speed will Qwen3.5 27B run at on RTX 6000 Ada 48GB?

On RTX 6000 Ada 48GB, Qwen3.5 27B achieves approximately 47.8 tokens per second decode speed with a time-to-first-token of 4051ms using Q4_K_M quantization.

Can RTX 6000 Ada 48GB run Qwen3.5 27B for coding?

For coding workloads, Qwen3.5 27B on RTX 6000 Ada 48GB receives a C grade with 47.8 tok/s and 129K context.

What context window can Qwen3.5 27B use on RTX 6000 Ada 48GB?

On RTX 6000 Ada 48GB, Qwen3.5 27B can safely use up to 129K 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 27B

Embed this result▼

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<iframe src="https://willitrunai.com/embed/hf-unsloth--qwen3-5-27b-gguf-on-rtx-6000-ada-48gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

Preview:

Workload

Grade

Fit

Decode

TTFT

Context

Chat

Runs well

47.8 tok/s

2209 ms

129K

Coding

Runs well

47.8 tok/s

4051 ms

129K

Agentic Coding

Runs well

47.8 tok/s

5892 ms

129K

Reasoning

Runs well

47.8 tok/s

4787 ms

129K

RAG

Runs well

47.8 tok/s

7365 ms

129K

Quantization options

How Qwen3.5 27B (27B params) fits at each quantization level on RTX 6000 Ada 48GB (48.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	10.5 GB	Low	C44
Q3_K_S	3	13.2 GB	Low	C44
NVFP4	4	15.1 GB	Medium	C45
Q4_K_M	4	16.5 GB	Medium	C45
Q5_K_M	5	19.4 GB	High	C46
Q6_K	6	22.1 GB	High	C47
Q8_0Best for your GPU	8	28.9 GB	Very High	C48
F16	16	55.4 GB	Maximum	F0

Frequently asked questions

Can RTX 6000 Ada 48GB run Qwen3.5 27B?

Yes, RTX 6000 Ada 48GB can run Qwen3.5 27B with a C grade (Runs well). Expected decode speed: 47.8 tok/s.

How much VRAM does Qwen3.5 27B need?

Qwen3.5 27B (27B parameters) requires approximately 25.6 GB of memory with Q4_K_M quantization.

What is the best quantization for Qwen3.5 27B?

The recommended quantization for Qwen3.5 27B is Q4_K_M, which balances quality and memory efficiency.

What speed will Qwen3.5 27B run at on RTX 6000 Ada 48GB?

On RTX 6000 Ada 48GB, Qwen3.5 27B achieves approximately 47.8 tokens per second decode speed with a time-to-first-token of 4051ms using Q4_K_M quantization.

Can RTX 6000 Ada 48GB run Qwen3.5 27B for coding?

For coding workloads, Qwen3.5 27B on RTX 6000 Ada 48GB receives a C grade with 47.8 tok/s and 129K context.

What context window can Qwen3.5 27B use on RTX 6000 Ada 48GB?

On RTX 6000 Ada 48GB, Qwen3.5 27B can safely use up to 129K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.