VOOZH about

URL: https://willitrunai.com/can-run/hf-unsloth--qwen3-5-35b-a3b-gguf-on-rx-7900-xt-20gb

⇱ Qwen3.5 35B A3B on RX 7900 XT 20GB? No — Alternatives

Can Qwen3.5 35B A3B run on RX 7900 XT 20GB?

YES — With Q2_K

C50Usable
○Estimated from fit model

Qwen3.5 35B A3B needs ~20.7 GB VRAM. RX 7900 XT 20GB has 20.0 GB. With Q2_K quantization, expect ~21 tok/s.

Runtime: llama.cppCapacity: OffloadBandwidth: HighStack: StandardBottleneck: Balanced
Share:

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 35B A3B at Q4_K_M needs 28.4 GB — too much for RX 7900 XT 20GB (20.0 GB). Runs at Q2_K (20.7 GB) with low quality.
Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 28.4 GB, exceeds 20.0 GB available
28.4 GB required20.0 GB available
142% VRAM needed

8.4 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

8.1 tok/s

TTFT

23934 ms

Safe context

4K

Memory

28.4 GB / 20.0 GB

Offload

30%

Memory breakdown

Weights21.3 GB
KV Cache4.1 GB
Runtime0.9 GB
Headroom2.0 GB

See how fast it feels

With memory offload — actual speed may be lower
See how fast it feelsQwen3.5 35B A3B 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: 8.1 tok/s decode · 23.9s TTFT (warm) · 20 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

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

Buy headroom, not only minimum fit

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

Performance by workload

WorkloadGradeFitDecodeTTFTContext
ChatFToo heavy9.5 tok/s11146 ms4K
CodingFToo heavy8.1 tok/s23934 ms4K
Agentic CodingFToo heavy6.1 tok/s46265 ms4K
ReasoningFToo heavy8.1 tok/s28285 ms4K
RAGFToo heavy6.1 tok/s57832 ms4K

Quantization options

How Qwen3.5 35B A3B (35B params) fits at each quantization level on RX 7900 XT 20GB (20.0 GB usable).

QuantBitsVRAMQualityFit
Q2_KBest for your GPU
2
13.7 GB
LowC50
Q3_K_S
3
17.2 GB
LowF0
NVFP4
4
19.6 GB
MediumF0
Q4_K_M
4
21.3 GB
MediumF0
Q5_K_M
5
25.2 GB
HighF0
Q6_K
6
28.7 GB
HighF0
Q8_0
8
37.5 GB
Very HighF0
F16
16
71.8 GB
MaximumF0

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

Upgrade options

Hardware that runs Qwen3.5 35B A3B well

RX 7900 XTX 24GBBest value
24 GB VRAM (+4)960 GB/s (+160)
D
Makes the model fit on the accelerator instead of staying completely out of reach.16.6 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Raises estimated decode speed by about 105%.

~$999 MSRP

Radeon AI PRO R9700 32GBBudget pick
32 GB VRAM (+12)
C
Makes the model fit on the accelerator instead of staying completely out of reach.17.7 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

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

~$1,899 MSRP

Radeon Pro W6800 32GBAMD upgrade
32 GB VRAM (+12)
C
Makes the model fit on the accelerator instead of staying completely out of reach.13.4 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

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

~$2,249 MSRP

👁 NVIDIA
NVIDIA A100 40GBBiggest leap
40 GB VRAM (+20)1555 GB/s (+755)
B
Makes the model fit on the accelerator instead of staying completely out of reach.61.2 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

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

~$10,000 MSRP

Frequently asked questions

See all results for RX 7900 XT 20GBSee all hardware for Qwen3.5 35B A3B