Can Qwen3.5 27B run on Quadro RTX 8000 48GB?

YES — Runs Great

C50Usable

Estimated from fit model

Qwen3.5 27B needs ~25.6 GB VRAM. Quadro RTX 8000 48GB has 48.0 GB. With Q4_K_M quantization, expect ~28 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: MediumStack: 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, 28.2 tok/s, Runs well

25.6 GB required48.0 GB available

53% VRAM used

Fit status

Runs well

Decode

28.2 tok/s

TTFT

6877 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 Quadro RTX 8000 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: 28.2 tok/s decode · 6.9s TTFT (warm) · 70 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

Older PCIe generation

PCIe 3.0 is workable, but it compounds the penalty when you offload heavily or try to scale across multiple cards.

Best improvement path

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	C	Runs well	28.2 tok/s	3751 ms	129K
Coding	C	Runs well	28.2 tok/s	6877 ms	129K
Agentic Coding	C	Runs well	28.2 tok/s	10002 ms	129K
Reasoning	C	Runs well	28.2 tok/s	8127 ms	129K
RAG	C	Runs well	28.2 tok/s	12503 ms	129K

Quantization options

How Qwen3.5 27B (27B params) fits at each quantization level on Quadro RTX 8000 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

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

Upgrade options

Hardware that runs Qwen3.5 27B well

NVIDIA A100 80GBBudget pick

80 GB VRAM (+32)2039 GB/s (+1367)

Raises estimated decode speed by about 269%.104 tok/s decode

Raises estimated decode speed by about 269%.

Adds memory headroom for longer context windows and future model growth.

~$15,000 MSRP

NVIDIA A800 80GBBest value

80 GB VRAM (+32)1935 GB/s (+1263)

Raises estimated decode speed by about 225%.91.6 tok/s decode

Raises estimated decode speed by about 225%.

Adds memory headroom for longer context windows and future model growth.

~$15,000 MSRP

NVIDIA H800 80GBNVIDIA upgrade

80 GB VRAM (+32)3000 GB/s (+2328)

Raises estimated decode speed by about 423%.147.5 tok/s decode

Raises estimated decode speed by about 423%.

Adds memory headroom for longer context windows and future model growth.

~$30,000 MSRP

Frequently asked questions

Can Quadro RTX 8000 48GB run Qwen3.5 27B?

Yes, Quadro RTX 8000 48GB can run Qwen3.5 27B with a C grade (Runs well). Expected decode speed: 28.2 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 Quadro RTX 8000 48GB?

On Quadro RTX 8000 48GB, Qwen3.5 27B achieves approximately 28.2 tokens per second decode speed with a time-to-first-token of 6877ms using Q4_K_M quantization.

Can Quadro RTX 8000 48GB run Qwen3.5 27B for coding?

For coding workloads, Qwen3.5 27B on Quadro RTX 8000 48GB receives a C grade with 28.2 tok/s and 129K context.

What context window can Qwen3.5 27B use on Quadro RTX 8000 48GB?

On Quadro RTX 8000 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 Quadro RTX 8000 48GB See all hardware for Qwen3.5 27B

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