Can StarCoder2 7B run on RX 6950 XT 16GB?

YES — Runs Great

C51Usable

Estimated from fit model

StarCoder2 7B needs ~7.3 GB VRAM. RX 6950 XT 16GB has 16.0 GB. With Q4_K_M quantization, expect ~85 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: MediumStack: 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) — 7.3 GB, 85.4 tok/s, Runs well

7.3 GB required16.0 GB available

46% VRAM used

Fit status

Runs well

Decode

85.4 tok/s

TTFT

2267 ms

Safe context

16K

Memory

7.3 GB / 16.0 GB

Memory breakdown

Weights4.3 GB

KV Cache0.5 GB

Runtime0.9 GB

Headroom1.6 GB

See how fast it feels

See how fast it feelsStarCoder2 7B on RX 6950 XT 16GB
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: 85.4 tok/s decode · 2.3s TTFT (warm) · 214 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	85.4 tok/s	1236 ms	16K
Coding	C	Runs well	85.4 tok/s	2267 ms	16K
Agentic Coding	C	Runs well	85.4 tok/s	3297 ms	16K
Reasoning	C	Runs well	85.4 tok/s	2679 ms	16K
RAG	C	Runs well	85.4 tok/s	4121 ms	16K

Inference speed

StarCoder2 7B inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for StarCoder2 7B at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~98 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	98.0	Fits
RTX 4090 24GB	24 GB	Q4_K_M	98.0	Fits
RTX 4080 Super 16GB	16 GB	Q4_K_M	98.0	Fits
RTX 3090 24GB	24 GB	Q4_K_M	98.0	Fits
RX 7900 XTX 24GB	24 GB	Q4_K_M	98.0	Fits
Mac Studio M3 Ultra 256GB	256 GB	Q4_K_M	98.0	Fits
Mac Studio M2 Ultra 128GB	128 GB	Q4_K_M	98.0	Fits
Mac Studio M1 Ultra 128GB	128 GB	Q4_K_M	98.0	Fits
RTX 4070 12GB	12 GB	Q4_K_M	96.6	Fits
MacBook Pro M4 Max 128GB	128 GB	Q4_K_M	95.9	Fits
MacBook Pro M4 Max 64GB	64 GB	Q4_K_M	95.9	Fits
MacBook Pro M3 Max 64GB	64 GB	Q4_K_M	61.4	Fits
RTX 3060 12GB	12 GB	Q4_K_M	60.7	Fits
MacBook Pro M1 Max 64GB	64 GB	Q4_K_M	56.2	Fits
MacBook Pro M4 Pro 48GB	48 GB	Q4_K_M	49.4	Fits
RTX 4060 8GB	8 GB	Q4_K_M	46.7	Fits

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 7B (7B params) fits at each quantization level on RX 6950 XT 16GB (16.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	2.7 GB	Low	C46
Q3_K_S	3	3.4 GB	Low	C47
NVFP4	4	3.9 GB	Medium	C47
Q4_K_M	4	4.3 GB	Medium	C47
Q5_K_M	5	5.0 GB	High	C48
Q6_K	6	5.7 GB	High	C49
Q8_0Best for your GPU	8	7.5 GB	Very High	C51
F16	16	14.3 GB	Maximum	F0

Get started

Copy-paste commands to run StarCoder2 7B on your machine.

Run

lms load starcoder2-7b && lms server start

Frequently asked questions

Can RX 6950 XT 16GB run StarCoder2 7B?

Yes, RX 6950 XT 16GB can run StarCoder2 7B with a C grade (Runs well). Expected decode speed: 85.4 tok/s.

How much VRAM does StarCoder2 7B need?

StarCoder2 7B (7B parameters) requires approximately 7.3 GB of memory with Q4_K_M quantization.

What is the best quantization for StarCoder2 7B?

The recommended quantization for StarCoder2 7B is Q4_K_M, which balances quality and memory efficiency.

What speed will StarCoder2 7B run at on RX 6950 XT 16GB?

On RX 6950 XT 16GB, StarCoder2 7B achieves approximately 85.4 tokens per second decode speed with a time-to-first-token of 2267ms using Q4_K_M quantization.

Can RX 6950 XT 16GB run StarCoder2 7B for coding?

For coding workloads, StarCoder2 7B on RX 6950 XT 16GB receives a C grade with 85.4 tok/s and 16K context.

What context window can StarCoder2 7B use on RX 6950 XT 16GB?

On RX 6950 XT 16GB, StarCoder2 7B can safely use up to 16K tokens of context. The model's official context limit is 16K, but available memory constrains the safe maximum.

See all results for RX 6950 XT 16GB See all hardware for StarCoder2 7B

Embed this result▼

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<iframe src="https://willitrunai.com/embed/starcoder2-7b-on-rx-6950-xt-16gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

Preview:

Workload

Grade

Fit

Decode

TTFT

Context

Chat

Runs well

85.4 tok/s

1236 ms

16K

Coding

Runs well

85.4 tok/s

2267 ms

16K

Agentic Coding

Runs well

85.4 tok/s

3297 ms

16K

Reasoning

Runs well

85.4 tok/s

2679 ms

16K

RAG

Runs well

85.4 tok/s

4121 ms

16K

StarCoder2 7B inference speed — tokens per second by GPU & Mac

GPU / Mac

Memory

Quant

Speed (tok/s)

Fits?

RTX 5090 32GB

32 GB

Q4_K_M

98.0

Fits

RTX 4090 24GB

24 GB

Q4_K_M

98.0

Fits

RTX 4080 Super 16GB

16 GB

Q4_K_M

98.0

Fits

RTX 3090 24GB

24 GB

Q4_K_M

98.0

Fits

RX 7900 XTX 24GB

24 GB

Q4_K_M

98.0

Fits

Mac Studio M3 Ultra 256GB

256 GB

Q4_K_M

98.0

Fits

Mac Studio M2 Ultra 128GB

128 GB

Q4_K_M

98.0

Fits

Mac Studio M1 Ultra 128GB

128 GB

Q4_K_M

98.0

Fits

RTX 4070 12GB

12 GB

Q4_K_M

96.6

Fits

MacBook Pro M4 Max 128GB

128 GB

Q4_K_M

95.9

Fits

MacBook Pro M4 Max 64GB

64 GB

Q4_K_M

95.9

Fits

MacBook Pro M3 Max 64GB

64 GB

Q4_K_M

61.4

Fits

RTX 3060 12GB

12 GB

Q4_K_M

60.7

Fits

MacBook Pro M1 Max 64GB

64 GB

Q4_K_M

56.2

Fits

MacBook Pro M4 Pro 48GB

48 GB

Q4_K_M

49.4

Fits

RTX 4060 8GB

8 GB

Q4_K_M

46.7

Fits

Quant

Bits

VRAM

Quality

Fit

Q2_K

2.7 GB

Low

C46

Q3_K_S

3.4 GB

Low

C47

NVFP4

3.9 GB

Medium

C47

Q4_K_M

4.3 GB

Medium

C47

Q5_K_M

5.0 GB

High

C48

Q6_K

5.7 GB

High

C49

Q8_0Best for your GPU

7.5 GB

Very High

C51

F16

14.3 GB

Maximum

Frequently asked questions

Can RX 6950 XT 16GB run StarCoder2 7B?

Yes, RX 6950 XT 16GB can run StarCoder2 7B with a C grade (Runs well). Expected decode speed: 85.4 tok/s.

How much VRAM does StarCoder2 7B need?

StarCoder2 7B (7B parameters) requires approximately 7.3 GB of memory with Q4_K_M quantization.

What is the best quantization for StarCoder2 7B?

The recommended quantization for StarCoder2 7B is Q4_K_M, which balances quality and memory efficiency.

What speed will StarCoder2 7B run at on RX 6950 XT 16GB?

On RX 6950 XT 16GB, StarCoder2 7B achieves approximately 85.4 tokens per second decode speed with a time-to-first-token of 2267ms using Q4_K_M quantization.

Can RX 6950 XT 16GB run StarCoder2 7B for coding?

For coding workloads, StarCoder2 7B on RX 6950 XT 16GB receives a C grade with 85.4 tok/s and 16K context.

What context window can StarCoder2 7B use on RX 6950 XT 16GB?

On RX 6950 XT 16GB, StarCoder2 7B can safely use up to 16K tokens of context. The model's official context limit is 16K, but available memory constrains the safe maximum.