Can CodeLlama 13B Instruct run on Radeon Pro W7800 32GB?

YES — Runs Great

A80Great

Estimated from fit model

CodeLlama 13B Instruct needs ~24.2 GB VRAM. Radeon Pro W7800 32GB has 32.0 GB. With Q4_K_M quantization, expect ~43 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) — 24.2 GB, 42.9 tok/s, Runs well

24.2 GB required32.0 GB available

76% VRAM used

Fit status

Runs well

Decode

42.9 tok/s

TTFT

4518 ms

Safe context

16K

Memory

24.2 GB / 32.0 GB

Memory breakdown

Weights7.9 GB

KV Cache12.2 GB

Runtime0.9 GB

Headroom3.2 GB

See how fast it feels

See how fast it feelsCodeLlama 13B Instruct on Radeon Pro W7800 32GB
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: 42.9 tok/s decode · 4.5s TTFT (warm) · 107 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	42.9 tok/s	2464 ms	16K
Coding	A	Runs well	42.9 tok/s	4518 ms	16K
Agentic Coding	B	Very compromised (needs ~1 GB host RAM)	24.4 tok/s	11520 ms	16K
Reasoning	A	Runs well	42.9 tok/s	5339 ms	16K
RAG	B	Very compromised (needs ~1 GB host RAM)	24.4 tok/s	14400 ms	16K

Inference speed

CodeLlama 13B Instruct inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for CodeLlama 13B Instruct at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~151 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	151.4	Fits
RTX 4090 24GB	24 GB	Q4_K_M	96.6	Offloads
RX 7900 XTX 24GB	24 GB	Q4_K_M	87.2	Offloads
RTX 3090 24GB	24 GB	Q4_K_M	82.6	Offloads
Mac Studio M3 Ultra 256GB	256 GB	Q4_K_M	70.2	Fits
Mac Studio M2 Ultra 128GB	128 GB	Q4_K_M	58.5	Fits
Mac Studio M1 Ultra 128GB	128 GB	Q4_K_M	55.5	Fits
MacBook Pro M4 Max 128GB	128 GB	Q4_K_M	38.2	Fits
MacBook Pro M4 Max 64GB	64 GB	Q4_K_M	38.2	Fits
MacBook Pro M3 Max 64GB	64 GB	Q4_K_M	30.3	Fits
MacBook Pro M1 Max 64GB	64 GB	Q4_K_M	27.7	Fits
RTX 4080 Super 16GB	16 GB	Q4_K_M	27.1	Too big
MacBook Pro M4 Pro 48GB	48 GB	Q4_K_M	23.3	Fits
RTX 4070 12GB	12 GB	Q4_K_M	9.5	Too big
RTX 3060 12GB	12 GB	Q4_K_M	6.0	Too big
RTX 4060 8GB	8 GB	Q4_K_M	3.8	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 13B Instruct (13B params) fits at each quantization level on Radeon Pro W7800 32GB (32.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	5.1 GB	Low	B69
Q3_K_S	3	6.4 GB	Low	B69
NVFP4	4	7.3 GB	Medium	B70
Q4_K_M	4	7.9 GB	Medium	B70
Q5_K_M	5	9.4 GB	High	A71
Q6_K	6	10.7 GB	High	A71
Q8_0	8	13.9 GB	Very High	A73
F16Best for your GPU	16	26.7 GB	Maximum	A74

Get started

Copy-paste commands to run CodeLlama 13B Instruct on your machine.

Run

lms load CodeLlama-13b-Instruct-hf && lms server start

Your hardware

More models your Radeon Pro W7800 32GB can run

Model	Params	Grade	Decode
Qwen3-Coder 30B A3B Instruct	30.5B	S	51.4 tok/s
Qwen 3.5 27B	27B	S	22.3 tok/s
Qwen 3.6 27B	27B	S	16.9 tok/s
Qwen 3.6 35B A3B	35B	S	43.2 tok/s
Qwen3-VL 30B A3B Instruct	30B	S	53.1 tok/s

Frequently asked questions

Can Radeon Pro W7800 32GB run CodeLlama 13B Instruct?

Yes, Radeon Pro W7800 32GB can run CodeLlama 13B Instruct with a A grade (Runs well). Expected decode speed: 42.9 tok/s.

How much VRAM does CodeLlama 13B Instruct need?

CodeLlama 13B Instruct (13B parameters) requires approximately 24.2 GB of memory with Q4_K_M quantization.

What is the best quantization for CodeLlama 13B Instruct?

The recommended quantization for CodeLlama 13B Instruct is Q4_K_M, which balances quality and memory efficiency.

What speed will CodeLlama 13B Instruct run at on Radeon Pro W7800 32GB?

On Radeon Pro W7800 32GB, CodeLlama 13B Instruct achieves approximately 42.9 tokens per second decode speed with a time-to-first-token of 4518ms using Q4_K_M quantization.

Can Radeon Pro W7800 32GB run CodeLlama 13B Instruct for coding?

For coding workloads, CodeLlama 13B Instruct on Radeon Pro W7800 32GB receives a A grade with 42.9 tok/s and 16K context.

What context window can CodeLlama 13B Instruct use on Radeon Pro W7800 32GB?

On Radeon Pro W7800 32GB, CodeLlama 13B 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 Radeon Pro W7800 32GB See all hardware for CodeLlama 13B Instruct

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