Will It Run AI

Can Qwen3.5 27B run on RX 7900 XT 20GB?

BARELY — Tight on Memory

D39Poor
Estimated from fit model

Qwen3.5 27B needs ~22.5 GB VRAM. RX 7900 XT 20GB has 20.0 GB. With Q4_K_M quantization, expect ~17 tok/s.

Runtime: llama.cppCapacity: OffloadBandwidth: HighStack: StandardBottleneck: Host offload
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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) — 22.5 GB, 17.0 tok/s, Very compromised (needs ~1.9 GB host RAM)
22.5 GB required20.0 GB available
113% VRAM needed

2.5 GB over capacity — needs offload or smaller quantization

Fit status

Very compromised (needs ~1.9 GB host RAM)

Decode

17.0 tok/s

TTFT

11386 ms

Safe context

4K

Memory

22.5 GB / 20.0 GB

Offload

10%

Memory breakdown

Weights16.5 GB
KV Cache3.2 GB
Runtime0.9 GB
Headroom2.0 GB

See how fast it feels

See how fast it feelsQwen3.5 27B on RX 7900 XT 20GB
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: 17.0 tok/s decode · 11.4s TTFT (warm) · 43 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 10% 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 {ram} GB of extra host RAM just for the offloaded portion, before OS and other tools.

Performance by workload

WorkloadGradeFitDecodeTTFTContext
ChatCRuns with offload (needs ~0.7 GB host RAM)19.8 tok/s5328 ms4K
CodingDVery compromised17.0 tok/s11386 ms4K
Agentic CodingFToo heavy12.9 tok/s21837 ms4K
ReasoningDVery compromised (needs ~1.9 GB host RAM)17.0 tok/s13456 ms4K
RAGFToo heavy12.9 tok/s27297 ms4K

Quantization options

How Qwen3.5 27B (27B params) fits at each quantization level on RX 7900 XT 20GB (20.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
10.5 GB
LowC51
Q3_K_S
3
13.2 GB
LowC51
NVFP4Best for your GPU
4
15.1 GB
MediumC50
Q4_K_M
4
16.5 GB
MediumF0
Q5_K_M
5
19.4 GB
HighF0
Q6_K
6
22.1 GB
HighF0
Q8_0
8
28.9 GB
Very HighF0
F16
16
55.4 GB
MaximumF0

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

アップグレードオプション

Qwen3.5 27Bを快適に動かすハードウェア

RX 7900 XTX 24GBコスパ重視
24 GB VRAM (+4)960 GB/s (+160)
C
Removes host-memory offload, which is usually the single biggest latency and throughput win.42 tok/sデコード

Removes host-memory offload, which is usually the single biggest latency and throughput win.

Raises estimated decode speed by about 147%.

〜$999 MSRP

Radeon AI PRO R9700 32GBコスパ最良
32 GB VRAM (+12)
C
Removes host-memory offload, which is usually the single biggest latency and throughput win.22.9 tok/sデコード

Removes host-memory offload, which is usually the single biggest latency and throughput win.

Raises estimated decode speed by about 35%.

〜$1,899 MSRP

Radeon Pro W6800 32GBAMDアップグレード
32 GB VRAM (+12)
C
Removes host-memory offload, which is usually the single biggest latency and throughput win.17.4 tok/sデコード

Removes host-memory offload, which is usually the single biggest latency and throughput win.

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

〜$2,249 MSRP

Frequently asked questions

Can RX 7900 XT 20GB run Qwen3.5 27B?

Yes, RX 7900 XT 20GB can run Qwen3.5 27B with a D grade (Very compromised). Expected decode speed: 17.0 tok/s.

How much VRAM does Qwen3.5 27B need?

Qwen3.5 27B (27B parameters) requires approximately 22.5 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 RX 7900 XT 20GB?

On RX 7900 XT 20GB, Qwen3.5 27B achieves approximately 17.0 tokens per second decode speed with a time-to-first-token of 11386ms using Q4_K_M quantization.

Can RX 7900 XT 20GB run Qwen3.5 27B for coding?

For coding workloads, Qwen3.5 27B on RX 7900 XT 20GB receives a D grade with 17.0 tok/s and 4K context.

What context window can Qwen3.5 27B use on RX 7900 XT 20GB?

On RX 7900 XT 20GB, Qwen3.5 27B can safely use up to 4K tokens of context. 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 RX 7900 XT 20GB?

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 RX 7900 XT 20GBSee all hardware for Qwen3.5 27B
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