Can CodeLlama 7B Instruct run on RX 9070 16GB?

YES — Tight Fit

A77Great

Estimated from fit model

CodeLlama 7B Instruct needs ~14.6 GB VRAM. RX 9070 16GB has 16.0 GB. With Q4_K_M quantization, expect ~93 tok/s.

Runtime: llama.cppCapacity: TightBandwidth: 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) — 14.6 GB, 92.9 tok/s, Tight fit

14.6 GB required16.0 GB available

91% VRAM used

Fit status

Tight fit

Decode

92.9 tok/s

TTFT

2083 ms

Safe context

16K

Memory

14.6 GB / 16.0 GB

Memory breakdown

Weights4.3 GB

KV Cache7.8 GB

Runtime0.9 GB

Headroom1.6 GB

See how fast it feels

See how fast it feelsCodeLlama 7B Instruct on RX 9070 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: 92.9 tok/s decode · 2.1s TTFT (warm) · 232 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	92.9 tok/s	1136 ms	16K
Coding	A	Tight fit	92.9 tok/s	2083 ms	16K
Agentic Coding	F	Too heavy	35.8 tok/s	7869 ms	16K
Reasoning	A	Tight fit	92.9 tok/s	2462 ms	16K
RAG	F	Too heavy	35.8 tok/s	9836 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 9070 16GB (16.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	2.7 GB	Low	A70
Q3_K_S	3	3.4 GB	Low	A71
NVFP4	4	3.9 GB	Medium	A71
Q4_K_M	4	4.3 GB	Medium	A71
Q5_K_M	5	5.0 GB	High	A72
Q6_K	6	5.7 GB	High	A73
Q8_0Best for your GPU	8	7.5 GB	Very High	A75
F16	16	14.3 GB	Maximum	F0

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 9070 16GB can run

Model	Params	Grade	Decode
Qwen 3.5 9B	9B	S	77.7 tok/s
Qwen 3 14B	14B	S	50.2 tok/s
Qwen 3 8B	8B	S	87.4 tok/s
Phi-4-reasoning-plus 14B	14.7B	S	47.6 tok/s
GPT-OSS 20B	21B	A	47.1 tok/s

Frequently asked questions

Can RX 9070 16GB run CodeLlama 7B Instruct?

Yes, RX 9070 16GB can run CodeLlama 7B Instruct with a A grade (Tight fit). Expected decode speed: 92.9 tok/s.

How much VRAM does CodeLlama 7B Instruct need?

CodeLlama 7B Instruct (7B parameters) requires approximately 14.6 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 9070 16GB?

On RX 9070 16GB, CodeLlama 7B Instruct achieves approximately 92.9 tokens per second decode speed with a time-to-first-token of 2083ms using Q4_K_M quantization.

Can RX 9070 16GB run CodeLlama 7B Instruct for coding?

For coding workloads, CodeLlama 7B Instruct on RX 9070 16GB receives a A grade with 92.9 tok/s and 16K context.

What context window can CodeLlama 7B Instruct use on RX 9070 16GB?

On RX 9070 16GB, 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 9070 16GB See all hardware for CodeLlama 7B Instruct

Embed this result▼

Paste this snippet into any page to show a live fit card.

<iframe src="https://willitrunai.com/embed/codellama-7b-instruct-on-rx-9070-16gb" 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

92.9 tok/s

1136 ms

16K

Coding

Tight fit

92.9 tok/s

2083 ms

16K

Agentic Coding

Too heavy

35.8 tok/s

7869 ms

16K

Reasoning

Tight fit

92.9 tok/s

2462 ms

16K

RAG

Too heavy

35.8 tok/s

9836 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 9070 16GB (16.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	2.7 GB	Low	A70
Q3_K_S	3	3.4 GB	Low	A71
NVFP4	4	3.9 GB	Medium	A71
Q4_K_M	4	4.3 GB	Medium	A71
Q5_K_M	5	5.0 GB	High	A72
Q6_K	6	5.7 GB	High	A73
Q8_0Best for your GPU	8	7.5 GB	Very High	A75
F16	16	14.3 GB	Maximum	F0

Model

Params

Grade

Decode

Capabilities

Qwen 3.5 9B

77.7 tok/s

Qwen 3 14B

14B

50.2 tok/s

Qwen 3 8B

87.4 tok/s

Phi-4-reasoning-plus 14B

14.7B

47.6 tok/s

GPT-OSS 20B

21B

47.1 tok/s

Frequently asked questions

Can RX 9070 16GB run CodeLlama 7B Instruct?

Yes, RX 9070 16GB can run CodeLlama 7B Instruct with a A grade (Tight fit). Expected decode speed: 92.9 tok/s.

How much VRAM does CodeLlama 7B Instruct need?

CodeLlama 7B Instruct (7B parameters) requires approximately 14.6 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 9070 16GB?

On RX 9070 16GB, CodeLlama 7B Instruct achieves approximately 92.9 tokens per second decode speed with a time-to-first-token of 2083ms using Q4_K_M quantization.

Can RX 9070 16GB run CodeLlama 7B Instruct for coding?

For coding workloads, CodeLlama 7B Instruct on RX 9070 16GB receives a A grade with 92.9 tok/s and 16K context.

What context window can CodeLlama 7B Instruct use on RX 9070 16GB?

On RX 9070 16GB, 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.