Can Qwen3-Coder 30B A3B Instruct run on RTX 6000 Ada 48GB?

YES — Runs Great

S97Excellent
Estimated from fit model

Qwen3-Coder 30B A3B Instruct needs ~26.1 GB VRAM. RTX 6000 Ada 48GB has 48.0 GB. With Q4_K_M quantization, expect ~119 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) 26.1 GB, 119.0 tok/s, Runs well
26.1 GB required48.0 GB available
54% VRAM used

Fit status

Runs well

Decode

119.0 tok/s

TTFT

1626 ms

Safe context

256K

Memory

26.1 GB / 48.0 GB

Memory breakdown

Weights18.6 GB
KV Cache1.5 GB
Runtime1.2 GB
Headroom4.8 GB

See how fast it feels

See how fast it feelsQwen3-Coder 30B A3B Instruct on RTX 6000 Ada 48GB
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: 119.0 tok/s decode · 1.6s TTFT (warm) · 298 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 well119.0 tok/s887 ms256K
CodingSRuns well119.0 tok/s1626 ms256K
Agentic CodingSRuns well119.0 tok/s2366 ms256K
ReasoningSRuns well119.0 tok/s1922 ms256K
RAGSRuns well119.0 tok/s2957 ms256K

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 6000 Ada 48GB (48.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
11.9 GB
LowS87
Q3_K_S
3
14.9 GB
LowS88
NVFP4
4
17.1 GB
MediumS88
Q4_K_M
4
18.6 GB
MediumS89
Q5_K_M
5
22.0 GB
HighS90
Q6_K
6
25.0 GB
HighS91
Q8_0Best for your GPU
8
32.6 GB
Very HighS91
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 6000 Ada 48GB run Qwen3-Coder 30B A3B Instruct?

Yes, RTX 6000 Ada 48GB can run Qwen3-Coder 30B A3B Instruct with a S grade (Runs well). Expected decode speed: 119.0 tok/s.

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

Qwen3-Coder 30B A3B Instruct (30.5B parameters) requires approximately 26.1 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 6000 Ada 48GB?

On RTX 6000 Ada 48GB, Qwen3-Coder 30B A3B Instruct achieves approximately 119.0 tokens per second decode speed with a time-to-first-token of 1626ms using Q4_K_M quantization.

Can RTX 6000 Ada 48GB run Qwen3-Coder 30B A3B Instruct for coding?

For coding workloads, Qwen3-Coder 30B A3B Instruct on RTX 6000 Ada 48GB receives a S grade with 119.0 tok/s and 256K context.

What context window can Qwen3-Coder 30B A3B Instruct use on RTX 6000 Ada 48GB?

On RTX 6000 Ada 48GB, Qwen3-Coder 30B A3B Instruct can safely use up to 256K tokens of context. The model's official context limit is 256K, but available memory constrains the safe maximum.

See all results for RTX 6000 Ada 48GBSee all hardware for Qwen3-Coder 30B A3B Instruct
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