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

YES — With Q2_K

C49Usable

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

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

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

Workload	Grade	Fit	Decode	TTFT	Context
Chat	F	Too heavy	9.5 tok/s	11146 ms	4K
Coding	F	Too heavy	8.1 tok/s	23934 ms	4K
Agentic Coding	F	Too heavy	6.1 tok/s	46265 ms	4K
Reasoning	F	Too heavy	8.1 tok/s	28285 ms	4K
RAG	F	Too heavy	6.1 tok/s	57832 ms	4K

Quantization options

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

Quant	Bits	VRAM	Quality	Fit
Q2_KBest for your GPU	2	13.7 GB	Low	C50
Q3_K_S	3	17.2 GB	Low	F0
NVFP4	4	19.6 GB	Medium	F0
Q4_K_M	4	21.3 GB	Medium	F0
Q5_K_M	5	25.2 GB	High	F0
Q6_K	6	28.7 GB	High	F0
Q8_0	8	37.5 GB	Very High	F0
F16	16	71.8 GB	Maximum	F0

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

Upgrade-Optionen

Hardware, die Qwen3.5 35B A3B gut ausführt

RX 7900 XTX 24GBBestes Preis-Leistungs-Verhältnis

24 GB VRAM (+4)960 GB/s (+160)

Makes the model fit on the accelerator instead of staying completely out of reach.16.6 tok/s Dekodierung

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

Raises estimated decode speed by about 105%.

ca. $999 MSRP

Radeon AI PRO R9700 32GBGünstige Wahl

32 GB VRAM (+12)

Makes the model fit on the accelerator instead of staying completely out of reach.17.7 tok/s Dekodierung

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.

ca. $1,899 MSRP

Radeon Pro W6800 32GBAMD-Upgrade

32 GB VRAM (+12)

Makes the model fit on the accelerator instead of staying completely out of reach.13.4 tok/s Dekodierung

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.

ca. $2,249 MSRP

NVIDIA A100 40GBGrößter Sprung

40 GB VRAM (+20)1555 GB/s (+755)

Makes the model fit on the accelerator instead of staying completely out of reach.61.2 tok/s Dekodierung

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.

ca. $10,000 MSRP

Frequently asked questions

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

Yes, RX 7900 XT 20GB can run Qwen3.5 35B A3B at Q2_K quantization (Runs with offload (needs ~0.4 GB host RAM)). The recommended Q4_K_M requires 28.4 GB which exceeds available memory, but at Q2_K it needs only 20.7 GB. Expected decode speed: 21.0 tok/s.

How much VRAM does Qwen3.5 35B A3B need?

Qwen3.5 35B A3B (35B parameters) requires approximately 28.4 GB at Q4_K_M quantization. On RX 7900 XT 20GB, it fits at Q2_K using 20.7 GB.

What is the best quantization for Qwen3.5 35B A3B?

The recommended quantization is Q4_K_M, but on RX 7900 XT 20GB the best fitting quantization is Q2_K, which uses 20.7 GB.

What speed will Qwen3.5 35B A3B run at on RX 7900 XT 20GB?

On RX 7900 XT 20GB, Qwen3.5 35B A3B achieves approximately 21.0 tokens per second decode speed with a time-to-first-token of 9239ms using Q2_K quantization.

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

For coding workloads, Qwen3.5 35B A3B on RX 7900 XT 20GB receives a F grade with 8.1 tok/s and 4K context.

What context window can Qwen3.5 35B A3B use on RX 7900 XT 20GB?

On RX 7900 XT 20GB, Qwen3.5 35B A3B can safely use up to 13K tokens of context at Q2_K quantization. The model's official context limit is —, but available memory constrains the safe maximum.

What should I upgrade first if Qwen3.5 35B A3B feels slow on RX 7900 XT 20GB?

Buy headroom, not only minimum fit. A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

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

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