willitrun·ai

Can StarCoder 15B run on Radeon PRO W7600 8GB?

NO — Won't Fit

F0Won't run
Estimated from fit model

StarCoder 15B needs ~27.4 GB but Radeon PRO W7600 8GB only has 8.0 GB. Try a smaller quantization or lighter model.

Runtime: OllamaCapacity: No fitBandwidth: LowStack: BasicBottleneck: Memory capacity
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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

Q5_K_M (High quality) — 27.4 GB, exceeds 8.0 GB available
27.4 GB required8.0 GB available
343% VRAM needed

19.4 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

2.4 tok/s

TTFT

80425 ms

Safe context

4K

Memory

27.4 GB / 8.0 GB

Offload

70%

Memory breakdown

Weights10.8 GB
KV Cache14.6 GB
Runtime1.2 GB
Headroom0.8 GB

See how fast it feels

With memory offload — actual speed may be lower
See how fast it feelsStarCoder 15B on Radeon PRO W7600 8GB
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: 2.4 tok/s decode · 80.4s TTFT (warm) · 6 tok/s prefill

What limits this setup

Usable VRAM is the main blocker for this model.

Not enough usable memory

The model needs 27.4 GB, but this setup only exposes 8.0 GB of usable VRAM.

Best improvement path

Add more VRAM headroom

The first useful upgrade is more dedicated VRAM so you can fit the model without shrinking context or dropping to a much lower quant.

Performance by workload

WorkloadGradeFitDecodeTTFTContext
ChatFToo heavy2.4 tok/s43868 ms4K
CodingFToo heavy2.4 tok/s80425 ms4K
Agentic CodingFToo heavy2.4 tok/s116982 ms4K
ReasoningFToo heavy2.4 tok/s95048 ms4K
RAGFToo heavy2.4 tok/s146228 ms4K

Inference speed

StarCoder 15B inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for StarCoder 15B at Q5_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~113 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 GBQ5_K_M113.4Tight
Mac Studio M3 Ultra 256GB
256 GBQ5_K_M52.6Fits
Mac Studio M2 Ultra 128GB
128 GBQ5_K_M43.8Fits
Mac Studio M1 Ultra 128GB
128 GBQ5_K_M41.6Fits
NVIDIARTX 4090 24GB
24 GBQ5_K_M36.3Too big
RX 7900 XTX 24GB
24 GBQ5_K_M32.8Too big
NVIDIARTX 3090 24GB
24 GBQ5_K_M31.1Too big
MacBook Pro M4 Max 128GB
128 GBQ5_K_M30.0Fits
MacBook Pro M4 Max 64GB
64 GBQ5_K_M30.0Fits
MacBook Pro M3 Max 64GB
64 GBQ5_K_M22.7Fits
MacBook Pro M1 Max 64GB
64 GBQ5_K_M20.8Fits
MacBook Pro M4 Pro 48GB
48 GBQ5_K_M18.3Tight
NVIDIARTX 4080 Super 16GB
16 GBQ5_K_M13.1Too big
NVIDIARTX 4070 12GB
12 GBQ5_K_M5.4Too big
NVIDIARTX 3060 12GB
12 GBQ5_K_M3.4Too big
NVIDIARTX 4060 8GB
8 GBQ5_K_M2.8Too big

Estimates for single-stream decoding at Q5_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 StarCoder 15B (15B params) fits at each quantization level on Radeon PRO W7600 8GB (8.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
5.9 GB
LowF0
Q3_K_S
3
7.4 GB
LowF0
NVFP4
4
8.4 GB
MediumF0
Q4_K_M
4
9.2 GB
MediumF0
Q5_K_M
5
10.8 GB
HighF0
Q6_K
6
12.3 GB
HighF0
Q8_0
8
16.1 GB
Very HighF0
F16
16
30.7 GB
MaximumF0

升级选项

能流畅运行 StarCoder 15B 的硬件

Radeon AI PRO R9700 32GB经济之选
32 GB VRAM (+24)640 GB/s (+352)
A
Makes the model fit on the accelerator instead of staying completely out of reach.35.7 tok/s 解码

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.

~$1,899 MSRP

Radeon Pro W6800 32GB最佳性价比
32 GB VRAM (+24)512 GB/s (+224)
A
Makes the model fit on the accelerator instead of staying completely out of reach.27.1 tok/s 解码

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.

~$2,249 MSRP

Radeon Pro W7800 32GBAMD 升级
32 GB VRAM (+24)576 GB/s (+288)
A
Makes the model fit on the accelerator instead of staying completely out of reach.32.1 tok/s 解码

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.

~$2,499 MSRP

NVIDIANVIDIA A100 40GB最大飞跃
40 GB VRAM (+32)1555 GB/s (+1267)
A
Makes the model fit on the accelerator instead of staying completely out of reach.123.4 tok/s 解码

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.

~$10,000 MSRP

Frequently asked questions

Can Radeon PRO W7600 8GB run StarCoder 15B?

No, StarCoder 15B requires more memory than Radeon PRO W7600 8GB provides.

How much VRAM does StarCoder 15B need?

StarCoder 15B (15B parameters) requires approximately 27.4 GB of memory with Q5_K_M quantization.

What is the best quantization for StarCoder 15B?

The recommended quantization for StarCoder 15B is Q5_K_M, which balances quality and memory efficiency.

What speed will StarCoder 15B run at on Radeon PRO W7600 8GB?

On Radeon PRO W7600 8GB, StarCoder 15B achieves approximately 2.4 tokens per second decode speed with a time-to-first-token of 80425ms using Q5_K_M quantization.

Can Radeon PRO W7600 8GB run StarCoder 15B for coding?

For coding workloads, StarCoder 15B on Radeon PRO W7600 8GB receives a F grade with 2.4 tok/s and 4K context.

What context window can StarCoder 15B use on Radeon PRO W7600 8GB?

On Radeon PRO W7600 8GB, StarCoder 15B can safely use up to 4K tokens of context. The model's official context limit is 8K, but available memory constrains the safe maximum.

What should I upgrade first if StarCoder 15B feels slow on Radeon PRO W7600 8GB?

Add more VRAM headroom. The first useful upgrade is more dedicated VRAM so you can fit the model without shrinking context or dropping to a much lower quant.

See all results for Radeon PRO W7600 8GBSee all hardware for StarCoder 15B
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