Can Qwen3.5 35B A3B run on RTX A5000 24GB?

BARELY — Tight on Memory

D32Poor

Estimated from fit model

Qwen3.5 35B A3B needs ~28.8 GB VRAM. RTX A5000 24GB has 24.0 GB. With Q4_K_M quantization, expect ~13 tok/s.

Runtime: llama.cppCapacity: OffloadBandwidth: MediumStack: 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, 12.9 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

12.9 tok/s

TTFT

14994 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 RTX A5000 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: 12.9 tok/s decode · 15.0s TTFT (warm) · 32 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)	15.1 tok/s	6999 ms	4K
Coding	D	Very compromised (needs ~3.5 GB host RAM)	12.9 tok/s	14994 ms	4K
Agentic Coding	F	Too heavy	9.8 tok/s	28877 ms	4K
Reasoning	D	Very compromised (needs ~3.5 GB host RAM)	12.9 tok/s	17720 ms	4K
RAG	F	Too heavy	9.8 tok/s	36097 ms	4K

Quantization options

How Qwen3.5 35B A3B (35B params) fits at each quantization level on RTX A5000 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
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

升级选项

能流畅运行 Qwen3.5 35B A3B 的硬件

RTX 5090 32GB经济之选

32 GB VRAM (+8)1792 GB/s (+1024)

Removes host-memory offload, which is usually the single biggest latency and throughput win.36.4 tok/s 解码

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

Raises estimated decode speed by about 182%.

~$1,999 MSRP

RTX PRO 4500 Blackwell 32GB最佳性价比

32 GB VRAM (+8)896 GB/s (+128)

Removes host-memory offload, which is usually the single biggest latency and throughput win.35.3 tok/s 解码

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

Raises estimated decode speed by about 174%.

~$2,499 MSRP

RTX 5000 Ada 32GBNVIDIA 升级

32 GB VRAM (+8)

Removes host-memory offload, which is usually the single biggest latency and throughput win.21.6 tok/s 解码

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

Raises estimated decode speed by about 67%.

~$4,000 MSRP

Frequently asked questions

Can RTX A5000 24GB run Qwen3.5 35B A3B?

Yes, RTX A5000 24GB can run Qwen3.5 35B A3B with a D grade (Very compromised (needs ~3.5 GB host RAM)). Expected decode speed: 12.9 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 RTX A5000 24GB?

On RTX A5000 24GB, Qwen3.5 35B A3B achieves approximately 12.9 tokens per second decode speed with a time-to-first-token of 14994ms using Q4_K_M quantization.

Can RTX A5000 24GB run Qwen3.5 35B A3B for coding?

For coding workloads, Qwen3.5 35B A3B on RTX A5000 24GB receives a D grade with 12.9 tok/s and 4K context.

What context window can Qwen3.5 35B A3B use on RTX A5000 24GB?

On RTX A5000 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 RTX A5000 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.

See all results for RTX A5000 24GB See all hardware for Qwen3.5 35B A3B

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