Can StarCoder2 15B run on Radeon Pro W7900 48GB?

YES — Runs Great

C48Usable

Estimated from fit model

StarCoder2 15B needs ~16.6 GB VRAM. Radeon Pro W7900 48GB has 48.0 GB. With Q4_K_M quantization, expect ~56 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: HighStack: StandardBottleneck: Balanced

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) — 16.6 GB, 55.7 tok/s, Runs well

16.6 GB required48.0 GB available

35% VRAM used

Fit status

Runs well

Decode

55.7 tok/s

TTFT

3475 ms

Safe context

302K

Memory

16.6 GB / 48.0 GB

Memory breakdown

Weights9.2 GB

KV Cache1.8 GB

Runtime0.9 GB

Headroom4.8 GB

See how fast it feels

See how fast it feelsStarCoder2 15B on Radeon Pro W7900 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: 55.7 tok/s decode · 3.5s TTFT (warm) · 139 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

Workload	Grade	Fit	Decode	TTFT	Context
Chat	C	Runs well	55.7 tok/s	1895 ms	302K
Coding	C	Runs well	55.7 tok/s	3475 ms	302K
Agentic Coding	C	Runs well	55.7 tok/s	5055 ms	302K
Reasoning	C	Runs well	55.7 tok/s	4107 ms	302K
RAG	C	Runs well	55.7 tok/s	6318 ms	302K

Inference speed

StarCoder2 15B inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for StarCoder2 15B at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~131 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 / Mac	Memory	Quant	Speed (tok/s)	Fits?
RTX 5090 32GB	32 GB	Q4_K_M	131.2	Fits
RTX 4090 24GB	24 GB	Q4_K_M	83.7	Fits
RX 7900 XTX 24GB	24 GB	Q4_K_M	75.5	Fits
RTX 3090 24GB	24 GB	Q4_K_M	71.6	Fits
RTX 4080 Super 16GB	16 GB	Q4_K_M	68.3	Tight
Mac Studio M3 Ultra 256GB	256 GB	Q4_K_M	60.9	Fits
Mac Studio M2 Ultra 128GB	128 GB	Q4_K_M	50.7	Fits
Mac Studio M1 Ultra 128GB	128 GB	Q4_K_M	48.1	Fits
MacBook Pro M4 Max 128GB	128 GB	Q4_K_M	34.7	Fits
MacBook Pro M4 Max 64GB	64 GB	Q4_K_M	34.7	Fits
RTX 4070 12GB	12 GB	Q4_K_M	26.7	Heavy offload
MacBook Pro M3 Max 64GB	64 GB	Q4_K_M	26.2	Fits
MacBook Pro M1 Max 64GB	64 GB	Q4_K_M	24.0	Fits
MacBook Pro M4 Pro 48GB	48 GB	Q4_K_M	21.2	Fits
RTX 3060 12GB	12 GB	Q4_K_M	15.7	Heavy offload
RTX 4060 8GB	8 GB	Q4_K_M	5.9	Too 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 StarCoder2 15B (15B params) fits at each quantization level on Radeon Pro W7900 48GB (48.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	5.9 GB	Low	C42
Q3_K_S	3	7.4 GB	Low	C42
NVFP4	4	8.4 GB	Medium	C42
Q4_K_M	4	9.2 GB	Medium	C43
Q5_K_M	5	10.8 GB	High	C43
Q6_K	6	12.3 GB	High	C43
Q8_0	8	16.1 GB	Very High	C45
F16Best for your GPU	16	30.7 GB	Maximum	C48

Get started

Copy-paste commands to run StarCoder2 15B on your machine.

Run

lms load hf-second-state--starcoder2-15b-gguf && lms server start

Opciones de mejora

Hardware que ejecuta bien StarCoder2 15B

AMD Instinct MI210 64GBOpción económica

64 GB VRAM (+16)1638 GB/s (+774)

Sube la velocidad estimada de decodificación alrededor de un 118%.121.7 tok/s decodificación

Sube la velocidad estimada de decodificación alrededor de un 118%.

Añade margen de memoria para más contexto y para que el modelo envejezca mejor.

~$10,000 MSRP

Frequently asked questions

Can Radeon Pro W7900 48GB run StarCoder2 15B?

Yes, Radeon Pro W7900 48GB can run StarCoder2 15B with a C grade (Runs well). Expected decode speed: 55.7 tok/s.

How much VRAM does StarCoder2 15B need?

StarCoder2 15B (15B parameters) requires approximately 16.6 GB of memory with Q4_K_M quantization.

What is the best quantization for StarCoder2 15B?

The recommended quantization for StarCoder2 15B is Q4_K_M, which balances quality and memory efficiency.

What speed will StarCoder2 15B run at on Radeon Pro W7900 48GB?

On Radeon Pro W7900 48GB, StarCoder2 15B achieves approximately 55.7 tokens per second decode speed with a time-to-first-token of 3475ms using Q4_K_M quantization.

Can Radeon Pro W7900 48GB run StarCoder2 15B for coding?

For coding workloads, StarCoder2 15B on Radeon Pro W7900 48GB receives a C grade with 55.7 tok/s and 302K context.

What context window can StarCoder2 15B use on Radeon Pro W7900 48GB?

On Radeon Pro W7900 48GB, StarCoder2 15B can safely use up to 302K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for Radeon Pro W7900 48GB See all hardware for StarCoder2 15B

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