Can Qwen3-Coder 30B A3B Instruct run on RTX 5090 32GB?

YES — Runs Great

S100Excellent
Estimated from fit model

Qwen3-Coder 30B A3B Instruct needs ~24.5 GB VRAM. RTX 5090 32GB has 32.0 GB. With Q4_K_M quantization, expect ~182 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: HighStack: BasicBottleneck: Balanced
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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) 24.5 GB, 181.6 tok/s, Runs well
24.5 GB required32.0 GB available
77% VRAM used

Fit status

Runs well

Decode

181.6 tok/s

TTFT

1066 ms

Safe context

98K

Memory

24.5 GB / 32.0 GB

Memory breakdown

Weights18.6 GB
KV Cache1.5 GB
Runtime1.2 GB
Headroom3.2 GB

See how fast it feels

See how fast it feelsQwen3-Coder 30B A3B Instruct on RTX 5090 32GB
1st promptCold start — includes initialization
>Write a Python function to sort a list
Here's a Python function that sorts a list using quicksort: def quicksort(arr): if len(arr) <= 1: return arr pivot = arr[len(arr) // 2] left = [x for x in arr if x < pivot] middle = [x for x in arr if x == pivot] right = [x for x in arr if x > pivot] return quicksort(left) + middle + quicksort(right) # Example usage numbers = [3, 6, 8, 10, 1, 2, 1] print(quicksort(numbers)) # [1, 1, 2, 3, 6, 8, 10] The algorithm picks a pivot, partitions the list into smaller, equal, and larger elements, then recursively sorts each partition and concatenates the results.
2nd promptWarm — model ready, no init overhead
>Now add type hints and a docstring
def quicksort(arr: list[int]) -> list[int]: """Sort a list using the quicksort algorithm. Args: arr: List of integers to sort. Returns: A new sorted list. """ if len(arr) <= 1: return arr pivot = arr[len(arr) // 2] left = [x for x in arr if x < pivot] middle = [x for x in arr if x == pivot] right = [x for x in arr if x > pivot] return quicksort(left) + middle + quicksort(right) Added type hints for the input and return type, plus a Google-style docstring.
Estimated: 181.6 tok/s decode · 1.1s TTFT (warm) · 454 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

No major red flags

This recommendation has enough memory headroom and acceptable estimated speed for the selected workload.

Best improvement path

Performance by workload

WorkloadGradeFitDecodeTTFTContext
ChatSRuns well181.6 tok/s582 ms98K
CodingSRuns well181.6 tok/s1066 ms98K
Agentic CodingSRuns well181.6 tok/s1551 ms98K
ReasoningSRuns well181.6 tok/s1260 ms98K
RAGSRuns well181.6 tok/s1939 ms98K

Inference speed

Qwen3-Coder 30B A3B Instruct inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for Qwen3-Coder 30B A3B Instruct at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~182 tok/s. Speed is memory-bandwidth bound, so cards that fit the whole model in VRAM run far faster than ones that offload to system RAM.

GPU / MacMemoryQuantSpeed (tok/s)Fits?
NVIDIARTX 5090 32GB
32 GBQ4_K_M181.6Fits
NVIDIARTX 4090 24GB
24 GBQ4_K_M115.8Offloads
RX 7900 XTX 24GB
24 GBQ4_K_M104.5Offloads
NVIDIARTX 3090 24GB
24 GBQ4_K_M99.1Offloads
Mac Studio M3 Ultra 256GB
256 GBQ4_K_M84.2Fits
Mac Studio M2 Ultra 128GB
128 GBQ4_K_M70.2Fits
Mac Studio M1 Ultra 128GB
128 GBQ4_K_M66.5Fits
MacBook Pro M4 Max 128GB
128 GBQ4_K_M52.0Fits
MacBook Pro M4 Max 64GB
64 GBQ4_K_M52.0Fits
MacBook Pro M3 Max 64GB
64 GBQ4_K_M36.3Fits
MacBook Pro M1 Max 64GB
64 GBQ4_K_M33.3Fits
NVIDIARTX 4080 Super 16GB
16 GBQ4_K_M32.7Too big
MacBook Pro M4 Pro 48GB
48 GBQ4_K_M31.8Fits
NVIDIARTX 4070 12GB
12 GBQ4_K_M11.4Too big
NVIDIARTX 3060 12GB
12 GBQ4_K_M7.2Too big
NVIDIARTX 4060 8GB
8 GBQ4_K_M4.5Too big

Estimates for single-stream decoding at Q4_K_M; real tokens/sec varies with prompt length, context, batch size, and runtime build. Prompt processing (prefill) is faster than the decode figures shown here.

Quantization options

How Qwen3-Coder 30B A3B Instruct (30.5B params) fits at each quantization level on RTX 5090 32GB (32.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
11.9 GB
LowS90
Q3_K_S
3
14.9 GB
LowS92
NVFP4
4
17.1 GB
MediumS93
Q4_K_M
4
18.6 GB
MediumS92
Q5_K_M
5
22.0 GB
HighS92
Q6_KBest for your GPU
6
25.0 GB
HighS92
Q8_0
8
32.6 GB
Very HighF0
F16
16
62.5 GB
MaximumF0

Get started

Copy-paste commands to run Qwen3-Coder 30B A3B Instruct on your machine.

Run

ollama run qwen3-coder

Frequently asked questions

Can RTX 5090 32GB run Qwen3-Coder 30B A3B Instruct?

Yes, RTX 5090 32GB can run Qwen3-Coder 30B A3B Instruct with a S grade (Runs well). Expected decode speed: 181.6 tok/s.

How much VRAM does Qwen3-Coder 30B A3B Instruct need?

Qwen3-Coder 30B A3B Instruct (30.5B parameters) requires approximately 24.5 GB of memory with Q4_K_M quantization.

What is the best quantization for Qwen3-Coder 30B A3B Instruct?

The recommended quantization for Qwen3-Coder 30B A3B Instruct is Q4_K_M, which balances quality and memory efficiency.

What speed will Qwen3-Coder 30B A3B Instruct run at on RTX 5090 32GB?

On RTX 5090 32GB, Qwen3-Coder 30B A3B Instruct achieves approximately 181.6 tokens per second decode speed with a time-to-first-token of 1066ms using Q4_K_M quantization.

Can RTX 5090 32GB run Qwen3-Coder 30B A3B Instruct for coding?

For coding workloads, Qwen3-Coder 30B A3B Instruct on RTX 5090 32GB receives a S grade with 181.6 tok/s and 98K context.

What context window can Qwen3-Coder 30B A3B Instruct use on RTX 5090 32GB?

On RTX 5090 32GB, Qwen3-Coder 30B A3B Instruct can safely use up to 98K tokens of context. The model's official context limit is 256K, but available memory constrains the safe maximum.

See all results for RTX 5090 32GBSee all hardware for Qwen3-Coder 30B A3B Instruct
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