Can Qwen3.5 27B run on RTX 4080 Super 16GB?

YES — With Q3_K_S

D34Poor

Estimated from fit model

Qwen3.5 27B needs ~19.2 GB VRAM. RTX 4080 Super 16GB has 16.0 GB. With Q3_K_S quantization, expect ~22 tok/s.

Runtime: OllamaCapacity: OffloadBandwidth: MediumStack: BasicBottleneck: Host offload

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.

Qwen3.5 27B at Q4_K_M needs 22.4 GB — too much for RTX 4080 Super 16GB (16.0 GB). Runs at Q3_K_S (19.2 GB) with low quality. 2 quantization levels fit.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 22.4 GB, exceeds 16.0 GB available

22.4 GB required16.0 GB available

140% VRAM needed

6.4 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

13.7 tok/s

TTFT

14177 ms

Safe context

Memory

22.4 GB / 16.0 GB

Offload

30%

Memory breakdown

Weights16.5 GB

KV Cache3.2 GB

Runtime1.2 GB

Headroom1.6 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsQwen3.5 27B on RTX 4080 Super 16GB
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: 13.7 tok/s decode · 14.2s TTFT (warm) · 34 tok/s prefill

What limits this setup

It fits through host-memory offload, and offload is the main reason performance drops.

CPU or host-memory offload is active

About 20% of the working set spills out of accelerator memory, which usually hurts latency and sustained decode throughput.

Very little memory headroom

You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.

Best improvement path

Remove offload with more accelerator memory

Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.

Buy headroom, not only minimum fit

A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

Increase host RAM if you keep offloading

This setup may need roughly 2.2 GB of extra host RAM just for the offloaded portion, before OS and other tools.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	F	Too heavy	15.9 tok/s	6629 ms	4K
Coding	F	Too heavy	13.7 tok/s	14177 ms	4K
Agentic Coding	F	Too heavy	10.3 tok/s	27222 ms	4K
Reasoning	F	Too heavy	13.7 tok/s	16754 ms	4K
RAG	F	Too heavy	10.3 tok/s	34027 ms	4K

Inference speed

Qwen3.5 27B inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for Qwen3.5 27B at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~73 tok/s. Speed is memory-bandwidth bound, so cards that fit the whole model in VRAM run far faster than ones that offload to system RAM.

GPU / Mac	Memory	Quant	Speed (tok/s)	Fits?
RTX 5090 32GB	32 GB	Q4_K_M	72.9	Fits
RTX 4090 24GB	24 GB	Q4_K_M	46.5	Offloads
RX 7900 XTX 24GB	24 GB	Q4_K_M	42.0	Offloads
RTX 3090 24GB	24 GB	Q4_K_M	39.8	Offloads
Mac Studio M3 Ultra 256GB	256 GB	Q4_K_M	33.8	Fits
MacBook Pro M4 Max 128GB	128 GB	Q4_K_M	33.4	Fits
MacBook Pro M4 Max 64GB	64 GB	Q4_K_M	33.4	Fits
Mac Studio M2 Ultra 128GB	128 GB	Q4_K_M	28.2	Fits
Mac Studio M1 Ultra 128GB	128 GB	Q4_K_M	26.7	Fits
MacBook Pro M4 Pro 48GB	48 GB	Q4_K_M	21.1	Fits
MacBook Pro M3 Max 64GB	64 GB	Q4_K_M	14.6	Fits
RTX 4080 Super 16GB	16 GB	Q4_K_M	13.7	Too big
MacBook Pro M1 Max 64GB	64 GB	Q4_K_M	13.4	Fits
RTX 4070 12GB	12 GB	Q4_K_M	4.8	Too big
RTX 3060 12GB	12 GB	Q4_K_M	3.0	Too big
RTX 4060 8GB	8 GB	Q4_K_M	2.0	Too big

Estimates for single-stream decoding at Q4_K_M; real tokens/sec varies with prompt length, context, batch size, and runtime build. Prompt processing (prefill) is faster than the decode figures shown here.

Quantization options

How Qwen3.5 27B (27B params) fits at each quantization level on RTX 4080 Super 16GB (16.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_KBest for your GPU	2	10.5 GB	Low	C51
Q3_K_S	3	13.2 GB	Low	F0
NVFP4	4	15.1 GB	Medium	F0
Q4_K_M	4	16.5 GB	Medium	F0
Q5_K_M	5	19.4 GB	High	F0
Q6_K	6	22.1 GB	High	F0
Q8_0	8	28.9 GB	Very High	F0
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

Opciones de mejora

Hardware que ejecuta bien Qwen3.5 27B

RTX 4000 Ada 20GBMejora NVIDIA

20 GB VRAM (+4)

Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.9.7 tok/s decodificación

Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.

Añade margen de memoria para más contexto y para que el modelo envejezca mejor.

~$1,250 MSRP

RTX 3090 24GBOpción económica

24 GB VRAM (+8)936 GB/s (+200)

Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.39.8 tok/s decodificación

Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.

Elimina el offload a memoria del sistema, que suele ser la mayor mejora individual en latencia y throughput.

~$1,499 MSRP

RTX 4090 24GBMejor relación calidad-precio

24 GB VRAM (+8)1008 GB/s (+272)

Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.46.5 tok/s decodificación

Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.

Elimina el offload a memoria del sistema, que suele ser la mayor mejora individual en latencia y throughput.

~$1,599 MSRP

Frequently asked questions

Can RTX 4080 Super 16GB run Qwen3.5 27B?

Yes, RTX 4080 Super 16GB can run Qwen3.5 27B at Q3_K_S quantization (Very compromised (needs ~2.2 GB host RAM)). The recommended Q4_K_M requires 22.4 GB which exceeds available memory, but at Q3_K_S it needs only 19.2 GB. Expected decode speed: 22.0 tok/s.

How much VRAM does Qwen3.5 27B need?

Qwen3.5 27B (27B parameters) requires approximately 22.4 GB at Q4_K_M quantization. On RTX 4080 Super 16GB, it fits at Q3_K_S using 19.2 GB.

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

The recommended quantization is Q4_K_M, but on RTX 4080 Super 16GB the best fitting quantization is Q3_K_S, which uses 19.2 GB.

What speed will Qwen3.5 27B run at on RTX 4080 Super 16GB?

On RTX 4080 Super 16GB, Qwen3.5 27B achieves approximately 22.0 tokens per second decode speed with a time-to-first-token of 8818ms using Q3_K_S quantization.

Can RTX 4080 Super 16GB run Qwen3.5 27B for coding?

For coding workloads, Qwen3.5 27B on RTX 4080 Super 16GB receives a F grade with 13.7 tok/s and 4K context.

What context window can Qwen3.5 27B use on RTX 4080 Super 16GB?

On RTX 4080 Super 16GB, Qwen3.5 27B can safely use up to 4K tokens of context at Q3_K_S quantization. The model's official context limit is —, but available memory constrains the safe maximum.

What should I upgrade first if Qwen3.5 27B feels slow on RTX 4080 Super 16GB?

Remove offload with more accelerator memory. Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.

See all results for RTX 4080 Super 16GB See all hardware for Qwen3.5 27B

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