Can StarCoder 7B run on RX 5600 XT 6GB?

NO — Won't Fit

F0Won't run

Estimated from fit model

StarCoder 7B needs ~13.1 GB but RX 5600 XT 6GB only has 6.0 GB. Try a smaller quantization or lighter model.

Runtime: llama.cppCapacity: No fitBandwidth: LowStack: StandardBottleneck: Memory capacity

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) — 13.1 GB, exceeds 6.0 GB available

13.1 GB required6.0 GB available

218% VRAM needed

7.1 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

5.3 tok/s

TTFT

36800 ms

Safe context

Memory

13.1 GB / 6.0 GB

Offload

50%

Memory breakdown

Weights4.3 GB

KV Cache7.3 GB

Runtime0.9 GB

Headroom0.6 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsStarCoder 7B on RX 5600 XT 6GB
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 · 36.8s TTFT (warm) · 13 tok/s prefill

What limits this setup

Usable VRAM is the main blocker for this model.

Not enough usable memory

The model needs 13.1 GB, but this setup only exposes 6.0 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

Workload	Grade	Fit	Decode	TTFT	Context
Chat	F	Too heavy	10.2 tok/s	10403 ms	4K
Coding	F	Too heavy	5.3 tok/s	36800 ms	4K
Agentic Coding	F	Too heavy	5.3 tok/s	53527 ms	4K
Reasoning	F	Too heavy	5.3 tok/s	43491 ms	4K
RAG	F	Too heavy	5.3 tok/s	66909 ms	4K

Inference speed

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

Estimated decode speed (tokens/sec) for StarCoder 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	Tight
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
MacBook Pro M4 Max 128GB	128 GB	Q4_K_M	87.8	Fits
MacBook Pro M4 Max 64GB	64 GB	Q4_K_M	87.8	Fits
MacBook Pro M3 Max 64GB	64 GB	Q4_K_M	56.2	Fits
MacBook Pro M1 Max 64GB	64 GB	Q4_K_M	51.5	Fits
RTX 4070 12GB	12 GB	Q4_K_M	48.0	Heavy offload
MacBook Pro M4 Pro 48GB	48 GB	Q4_K_M	45.3	Fits
RTX 3060 12GB	12 GB	Q4_K_M	30.2	Heavy offload
RTX 4060 8GB	8 GB	Q4_K_M	11.4	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 StarCoder 7B (7B params) fits at each quantization level on RX 5600 XT 6GB (6.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	2.7 GB	Low	A77
Q3_K_SBest for your GPU	3	3.4 GB	Low	A77
NVFP4	4	3.9 GB	Medium	F0
Q4_K_M	4	4.3 GB	Medium	F0
Q5_K_M	5	5.0 GB	High	F0
Q6_K	6	5.7 GB	High	F0
Q8_0	8	7.5 GB	Very High	F0
F16	16	14.3 GB	Maximum	F0

Upgrade options

Hardware that runs StarCoder 7B well

RX 7600 XT 16GBBudget pick

16 GB VRAM (+10)

Makes the model fit on the accelerator instead of staying completely out of reach.39.1 tok/s decode

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 16GBBest value

16 GB VRAM (+10)320 GB/s (+32)

Makes the model fit on the accelerator instead of staying completely out of reach.47.2 tok/s decode

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 12GBAMD upgrade

12 GB VRAM (+6)432 GB/s (+144)

Makes the model fit on the accelerator instead of staying completely out of reach.34.5 tok/s decode

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

Raises estimated decode speed by about 551%.

~$449 MSRP

RX 7900 XT 20GBBiggest leap

20 GB VRAM (+14)800 GB/s (+512)

Makes the model fit on the accelerator instead of staying completely out of reach.98 tok/s decode

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.

~$899 MSRP

Frequently asked questions

Can RX 5600 XT 6GB run StarCoder 7B?

No, StarCoder 7B requires more memory than RX 5600 XT 6GB provides.

How much VRAM does StarCoder 7B need?

StarCoder 7B (7B parameters) requires approximately 13.1 GB of memory with Q4_K_M quantization.

What is the best quantization for StarCoder 7B?

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

What speed will StarCoder 7B run at on RX 5600 XT 6GB?

On RX 5600 XT 6GB, StarCoder 7B achieves approximately 5.3 tokens per second decode speed with a time-to-first-token of 36800ms using Q4_K_M quantization.

Can RX 5600 XT 6GB run StarCoder 7B for coding?

For coding workloads, StarCoder 7B on RX 5600 XT 6GB receives a F grade with 5.3 tok/s and 4K context.

What context window can StarCoder 7B use on RX 5600 XT 6GB?

On RX 5600 XT 6GB, StarCoder 7B 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 7B feels slow on RX 5600 XT 6GB?

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 RX 5600 XT 6GB See all hardware for StarCoder 7B

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Preview:

Workload

Grade

Fit

Decode

TTFT

Context

Chat

Too heavy

10.2 tok/s

10403 ms

Coding

Too heavy

5.3 tok/s

36800 ms

Agentic Coding

Too heavy

5.3 tok/s

53527 ms

Reasoning

Too heavy

5.3 tok/s

43491 ms

RAG

Too heavy

5.3 tok/s

66909 ms

StarCoder 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

Tight

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

MacBook Pro M4 Max 128GB

128 GB

Q4_K_M

87.8

Fits

MacBook Pro M4 Max 64GB

64 GB

Q4_K_M

87.8

Fits

MacBook Pro M3 Max 64GB

64 GB

Q4_K_M

56.2

Fits

MacBook Pro M1 Max 64GB

64 GB

Q4_K_M

51.5

Fits

RTX 4070 12GB

12 GB

Q4_K_M

48.0

Heavy offload

MacBook Pro M4 Pro 48GB

48 GB

Q4_K_M

45.3

Fits

RTX 3060 12GB

12 GB

Q4_K_M

30.2

Heavy offload

RTX 4060 8GB

8 GB

Q4_K_M

11.4

Too big

Quant

Bits

VRAM

Quality

Fit

Q2_K

2.7 GB

Low

A77

Q3_K_SBest for your GPU

3.4 GB

Low

A77

NVFP4

3.9 GB

Medium

Q4_K_M

4.3 GB

Medium

Q5_K_M

5.0 GB

High

Q6_K

5.7 GB

High

Q8_0

7.5 GB

Very High

F16

14.3 GB

Maximum

Frequently asked questions

Can RX 5600 XT 6GB run StarCoder 7B?

No, StarCoder 7B requires more memory than RX 5600 XT 6GB provides.

How much VRAM does StarCoder 7B need?

StarCoder 7B (7B parameters) requires approximately 13.1 GB of memory with Q4_K_M quantization.

What is the best quantization for StarCoder 7B?

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

What speed will StarCoder 7B run at on RX 5600 XT 6GB?

On RX 5600 XT 6GB, StarCoder 7B achieves approximately 5.3 tokens per second decode speed with a time-to-first-token of 36800ms using Q4_K_M quantization.

Can RX 5600 XT 6GB run StarCoder 7B for coding?

For coding workloads, StarCoder 7B on RX 5600 XT 6GB receives a F grade with 5.3 tok/s and 4K context.

What context window can StarCoder 7B use on RX 5600 XT 6GB?

On RX 5600 XT 6GB, StarCoder 7B 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 7B feels slow on RX 5600 XT 6GB?

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.