Can starcoder2 15b instruct v0.1 run on Radeon PRO W7600 8GB?

YES — With Q2_K

D37Poor

Estimated from fit model

starcoder2 15b instruct v0.1 needs ~9.3 GB VRAM. Radeon PRO W7600 8GB has 8.0 GB. With Q2_K quantization, expect ~14 tok/s.

Runtime: llama.cppCapacity: OffloadBandwidth: LowStack: 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.

starcoder2 15b instruct v0.1 at Q4_K_M needs 12.6 GB — too much for Radeon PRO W7600 8GB (8.0 GB). Runs at Q2_K (9.3 GB) with low quality.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 12.6 GB, exceeds 8.0 GB available

12.6 GB required8.0 GB available

158% VRAM needed

4.6 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

5.3 tok/s

TTFT

36213 ms

Safe context

Memory

12.6 GB / 8.0 GB

Offload

40%

Memory breakdown

Weights9.2 GB

KV Cache1.8 GB

Runtime0.9 GB

Headroom0.8 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsstarcoder2 15b instruct v0.1 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: 5.3 tok/s decode · 36.2s TTFT (warm) · 13 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 10% 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 0.8 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	F	Too heavy	6.2 tok/s	16965 ms	4K
Coding	F	Too heavy	5.3 tok/s	36213 ms	4K
Agentic Coding	F	Too heavy	4.1 tok/s	69328 ms	4K
Reasoning	F	Too heavy	5.3 tok/s	42797 ms	4K
RAG	F	Too heavy	4.1 tok/s	86661 ms	4K

Inference speed

starcoder2 15b instruct v0.1 inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for starcoder2 15b instruct v0.1 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 instruct v0.1 (15B params) fits at each quantization level on Radeon PRO W7600 8GB (8.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	5.9 GB	Low	F0
Q3_K_S	3	7.4 GB	Low	F0
NVFP4	4	8.4 GB	Medium	F0
Q4_K_M	4	9.2 GB	Medium	F0
Q5_K_M	5	10.8 GB	High	F0
Q6_K	6	12.3 GB	High	F0
Q8_0	8	16.1 GB	Very High	F0
F16	16	30.7 GB	Maximum	F0

Get started

Copy-paste commands to run starcoder2 15b instruct v0.1 on your machine.

Run

lms load hf-bartowski--starcoder2-15b-instruct-v0-1-gguf && lms server start

Opções de upgrade

Hardware que roda bem starcoder2 15b instruct v0.1

RX 7600 XT 16GBOpção econômica

16 GB VRAM (+8)

Makes the model fit on the accelerator instead of staying completely out of reach.18.3 tok/s decodificação

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.

~$329 MSRP

RX 9060 XT 16GBMelhor custo-benefício

16 GB VRAM (+8)320 GB/s (+32)

Makes the model fit on the accelerator instead of staying completely out of reach.22 tok/s decodificação

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.

~$349 MSRP

RX 7700 XT 12GBUpgrade AMD

12 GB VRAM (+4)432 GB/s (+144)

Makes the model fit on the accelerator instead of staying completely out of reach.17.9 tok/s decodificação

Makes the model fit on the accelerator instead of staying completely out of reach.

Raises estimated decode speed by about 238%.

~$449 MSRP

RTX A4500 20GBMaior salto

20 GB VRAM (+12)640 GB/s (+352)

Makes the model fit on the accelerator instead of staying completely out of reach.54.6 tok/s decodificação

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.

~$2,000 MSRP

Frequently asked questions

Can Radeon PRO W7600 8GB run starcoder2 15b instruct v0.1?

Yes, Radeon PRO W7600 8GB can run starcoder2 15b instruct v0.1 at Q2_K quantization (Very compromised (needs ~0.8 GB host RAM)). The recommended Q4_K_M requires 12.6 GB which exceeds available memory, but at Q2_K it needs only 9.3 GB. Expected decode speed: 13.5 tok/s.

How much VRAM does starcoder2 15b instruct v0.1 need?

starcoder2 15b instruct v0.1 (15B parameters) requires approximately 12.6 GB at Q4_K_M quantization. On Radeon PRO W7600 8GB, it fits at Q2_K using 9.3 GB.

What is the best quantization for starcoder2 15b instruct v0.1?

The recommended quantization is Q4_K_M, but on Radeon PRO W7600 8GB the best fitting quantization is Q2_K, which uses 9.3 GB.

What speed will starcoder2 15b instruct v0.1 run at on Radeon PRO W7600 8GB?

On Radeon PRO W7600 8GB, starcoder2 15b instruct v0.1 achieves approximately 13.5 tokens per second decode speed with a time-to-first-token of 14380ms using Q2_K quantization.

Can Radeon PRO W7600 8GB run starcoder2 15b instruct v0.1 for coding?

For coding workloads, starcoder2 15b instruct v0.1 on Radeon PRO W7600 8GB receives a F grade with 5.3 tok/s and 4K context.

What context window can starcoder2 15b instruct v0.1 use on Radeon PRO W7600 8GB?

On Radeon PRO W7600 8GB, starcoder2 15b instruct v0.1 can safely use up to 4K tokens of context at Q2_K quantization. The model's official context limit is —, but available memory constrains the safe maximum.

What should I upgrade first if starcoder2 15b instruct v0.1 feels slow on Radeon PRO W7600 8GB?

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 Radeon PRO W7600 8GB See all hardware for starcoder2 15b instruct v0.1

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