Can CodeLlama 7B Instruct run on RX 7900 XT 20GB?

YES — Runs Great

A80Great

Estimated from fit model

CodeLlama 7B Instruct needs ~15.0 GB VRAM. RX 7900 XT 20GB has 20.0 GB. With Q4_K_M quantization, expect ~98 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) — 15.0 GB, 98.0 tok/s, Runs well

15.0 GB required20.0 GB available

75% VRAM used

Fit status

Runs well

Decode

98.0 tok/s

TTFT

1976 ms

Safe context

16K

Memory

15.0 GB / 20.0 GB

Memory breakdown

Weights4.3 GB

KV Cache7.8 GB

Runtime0.9 GB

Headroom2.0 GB

See how fast it feels

See how fast it feelsCodeLlama 7B Instruct on RX 7900 XT 20GB
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: 98.0 tok/s decode · 2.0s TTFT (warm) · 245 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	A	Runs well	98.0 tok/s	1078 ms	16K
Coding	A	Runs well	98.0 tok/s	1976 ms	16K
Agentic Coding	B	Very compromised (needs ~0.5 GB host RAM)	64.0 tok/s	4399 ms	16K
Reasoning	A	Runs well	98.0 tok/s	2335 ms	16K
RAG	B	Very compromised (needs ~0.5 GB host RAM)	64.0 tok/s	5499 ms	16K

Inference speed

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

Estimated decode speed (tokens/sec) for CodeLlama 7B Instruct 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
MacBook Pro M4 Pro 48GB	48 GB	Q4_K_M	45.3	Fits
RTX 4070 12GB	12 GB	Q4_K_M	43.0	Heavy offload
RTX 3060 12GB	12 GB	Q4_K_M	24.7	Heavy offload
RTX 4060 8GB	8 GB	Q4_K_M	10.6	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 CodeLlama 7B Instruct (7B params) fits at each quantization level on RX 7900 XT 20GB (20.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	2.7 GB	Low	B69
Q3_K_S	3	3.4 GB	Low	B69
NVFP4	4	3.9 GB	Medium	B69
Q4_K_M	4	4.3 GB	Medium	B70
Q5_K_M	5	5.0 GB	High	A70
Q6_K	6	5.7 GB	High	A71
Q8_0	8	7.5 GB	Very High	A72
F16Best for your GPU	16	14.3 GB	Maximum	A73

Get started

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

Run

lms load CodeLlama-7b-Instruct-hf && lms server start

Your hardware

More models your RX 7900 XT 20GB can run

Model	Params	Grade	Decode
Qwen3-Coder 30B A3B Instruct	30.5B	A	40.7 tok/s
Qwen 3.5 27B	27B	A	18.3 tok/s
Qwen 3.6 27B	27B	S	17.3 tok/s
Qwen3-VL 30B A3B Instruct	30B	A	43.3 tok/s
Qwen 3.5 9B	9B	S	94 tok/s

Frequently asked questions

Can RX 7900 XT 20GB run CodeLlama 7B Instruct?

Yes, RX 7900 XT 20GB can run CodeLlama 7B Instruct with a A grade (Runs well). Expected decode speed: 98.0 tok/s.

How much VRAM does CodeLlama 7B Instruct need?

CodeLlama 7B Instruct (7B parameters) requires approximately 15.0 GB of memory with Q4_K_M quantization.

What is the best quantization for CodeLlama 7B Instruct?

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

What speed will CodeLlama 7B Instruct run at on RX 7900 XT 20GB?

On RX 7900 XT 20GB, CodeLlama 7B Instruct achieves approximately 98.0 tokens per second decode speed with a time-to-first-token of 1976ms using Q4_K_M quantization.

Can RX 7900 XT 20GB run CodeLlama 7B Instruct for coding?

For coding workloads, CodeLlama 7B Instruct on RX 7900 XT 20GB receives a A grade with 98.0 tok/s and 16K context.

What context window can CodeLlama 7B Instruct use on RX 7900 XT 20GB?

On RX 7900 XT 20GB, CodeLlama 7B Instruct 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 7900 XT 20GB See all hardware for CodeLlama 7B Instruct

Embed this result▼

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<iframe src="https://willitrunai.com/embed/codellama-7b-instruct-on-rx-7900-xt-20gb" 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

98.0 tok/s

1078 ms

16K

Coding

Runs well

98.0 tok/s

1976 ms

16K

Agentic Coding

Very compromised (needs ~0.5 GB host RAM)

64.0 tok/s

4399 ms

16K

Reasoning

Runs well

98.0 tok/s

2335 ms

16K

RAG

Very compromised (needs ~0.5 GB host RAM)

64.0 tok/s

5499 ms

16K

CodeLlama 7B Instruct 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

MacBook Pro M4 Pro 48GB

48 GB

Q4_K_M

45.3

Fits

RTX 4070 12GB

12 GB

Q4_K_M

43.0

Heavy offload

RTX 3060 12GB

12 GB

Q4_K_M

24.7

Heavy offload

RTX 4060 8GB

8 GB

Q4_K_M

10.6

Too big

Quantization options

How CodeLlama 7B Instruct (7B params) fits at each quantization level on RX 7900 XT 20GB (20.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	2.7 GB	Low	B69
Q3_K_S	3	3.4 GB	Low	B69
NVFP4	4	3.9 GB	Medium	B69
Q4_K_M	4	4.3 GB	Medium	B70
Q5_K_M	5	5.0 GB	High	A70
Q6_K	6	5.7 GB	High	A71
Q8_0	8	7.5 GB	Very High	A72
F16Best for your GPU	16	14.3 GB	Maximum	A73

Model

Params

Grade

Decode

Capabilities

Qwen3-Coder 30B A3B Instruct

30.5B

40.7 tok/s

Qwen 3.5 27B

27B

18.3 tok/s

Qwen 3.6 27B

27B

17.3 tok/s

Qwen3-VL 30B A3B Instruct

30B

43.3 tok/s

Qwen 3.5 9B

94 tok/s

Frequently asked questions

Can RX 7900 XT 20GB run CodeLlama 7B Instruct?

Yes, RX 7900 XT 20GB can run CodeLlama 7B Instruct with a A grade (Runs well). Expected decode speed: 98.0 tok/s.

How much VRAM does CodeLlama 7B Instruct need?

CodeLlama 7B Instruct (7B parameters) requires approximately 15.0 GB of memory with Q4_K_M quantization.

What is the best quantization for CodeLlama 7B Instruct?

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

What speed will CodeLlama 7B Instruct run at on RX 7900 XT 20GB?

On RX 7900 XT 20GB, CodeLlama 7B Instruct achieves approximately 98.0 tokens per second decode speed with a time-to-first-token of 1976ms using Q4_K_M quantization.

Can RX 7900 XT 20GB run CodeLlama 7B Instruct for coding?

For coding workloads, CodeLlama 7B Instruct on RX 7900 XT 20GB receives a A grade with 98.0 tok/s and 16K context.

What context window can CodeLlama 7B Instruct use on RX 7900 XT 20GB?

On RX 7900 XT 20GB, CodeLlama 7B Instruct can safely use up to 16K tokens of context. The model's official context limit is 16K, but available memory constrains the safe maximum.