Can Qwen3.5 35B A3B run on RX 7900 XTX 24GB?

Q: What should I upgrade first if Qwen3.5 35B A3B feels slow on RX 7900 XTX 24GB?

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.

BARELY — Tight on Memory

D33Poor

Estimated from fit model

Qwen3.5 35B A3B needs ~28.8 GB VRAM. RX 7900 XTX 24GB has 24.0 GB. With Q4_K_M quantization, expect ~17 tok/s.

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

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 28.8 GB, 16.6 tok/s, Very compromised (needs ~3.5 GB host RAM)

28.8 GB required24.0 GB available

120% VRAM needed

4.8 GB over capacity — needs offload or smaller quantization

Fit status

Very compromised (needs ~3.5 GB host RAM)

Decode

16.6 tok/s

TTFT

11662 ms

Safe context

Memory

28.8 GB / 24.0 GB

Offload

20%

Memory breakdown

Weights21.3 GB

KV Cache4.1 GB

Runtime0.9 GB

Headroom2.4 GB

See how fast it feels

See how fast it feelsQwen3.5 35B A3B on RX 7900 XTX 24GB
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: 16.6 tok/s decode · 11.7s TTFT (warm) · 42 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 3.5 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	D	Very compromised (needs ~2.2 GB host RAM)	19.4 tok/s	5444 ms	4K
Coding	D	Very compromised (needs ~3.5 GB host RAM)	16.6 tok/s	11662 ms	4K
Agentic Coding	F	Too heavy	12.5 tok/s	22460 ms	4K
Reasoning	D	Very compromised (needs ~3.5 GB host RAM)	16.6 tok/s	13782 ms	4K
RAG	F	Too heavy	12.5 tok/s	28075 ms

Quantization options

How Qwen3.5 35B A3B (35B params) fits at each quantization level on RX 7900 XTX 24GB (24.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	13.7 GB	Low	C50
Q3_K_SBest for your GPU	3	17.2 GB	Low	C50

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 options

Hardware that runs Qwen3.5 35B A3B well

Radeon AI PRO R9700 32GBBudget pick

32 GB VRAM (+8)

Removes host-memory offload, which is usually the single biggest latency and throughput win.17.7 tok/s decode

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.

~$1,899 MSRP

Radeon Pro W6800 32GBBest value

32 GB VRAM (+8)

Removes host-memory offload, which is usually the single biggest latency and throughput win.13.4 tok/s decode

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

Radeon Pro W7800 32GBAMD upgrade

32 GB VRAM (+8)

Removes host-memory offload, which is usually the single biggest latency and throughput win.15.9 tok/s decode

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.

~$2,499 MSRP

NVIDIA A100 40GBBiggest leap

40 GB VRAM (+16)1555 GB/s (+595)

Removes host-memory offload, which is usually the single biggest latency and throughput win.61.2 tok/s decode

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

Raises estimated decode speed by about 269%.

~$10,000 MSRP

Frequently asked questions

Can RX 7900 XTX 24GB run Qwen3.5 35B A3B?

Yes, RX 7900 XTX 24GB can run Qwen3.5 35B A3B with a D grade (Very compromised (needs ~3.5 GB host RAM)). Expected decode speed: 16.6 tok/s.

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

Qwen3.5 35B A3B (35B parameters) requires approximately 28.8 GB of memory with Q4_K_M quantization.

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

The recommended quantization for Qwen3.5 35B A3B is Q4_K_M, which balances quality and memory efficiency.

What speed will Qwen3.5 35B A3B run at on RX 7900 XTX 24GB?

On RX 7900 XTX 24GB, Qwen3.5 35B A3B achieves approximately 16.6 tokens per second decode speed with a time-to-first-token of 11662ms using Q4_K_M quantization.

Can RX 7900 XTX 24GB run Qwen3.5 35B A3B for coding?

For coding workloads, Qwen3.5 35B A3B on RX 7900 XTX 24GB receives a D grade with 16.6 tok/s and 4K context.

What context window can Qwen3.5 35B A3B use on RX 7900 XTX 24GB?

On RX 7900 XTX 24GB, Qwen3.5 35B A3B 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 35B A3B feels slow on RX 7900 XTX 24GB?

See all results for RX 7900 XTX 24GB See all hardware for Qwen3.5 35B A3B

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