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URL: https://willitrunai.com/can-run/codellama-7b-instruct-on-radeon-pro-w7500-8gb

⇱ CodeLlama 7B Instruct on Radeon Pro W7500 8GB? No — Alterna…

Can CodeLlama 7B Instruct run on Radeon Pro W7500 8GB?

NO — Won't Fit

F0Won't run
○Estimated from fit model

CodeLlama 7B Instruct needs ~13.8 GB but Radeon Pro W7500 8GB only has 8.0 GB. Try a smaller quantization or lighter model.

Runtime: llama.cppCapacity: No fitBandwidth: Very lowStack: StandardBottleneck: Memory capacity
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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) — 13.8 GB, exceeds 8.0 GB available
13.8 GB required8.0 GB available
173% VRAM needed

5.8 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

7.4 tok/s

TTFT

26209 ms

Safe context

4K

Memory

13.8 GB / 8.0 GB

Offload

40%

Memory breakdown

Weights4.3 GB
KV Cache7.8 GB
Runtime0.9 GB
Headroom0.8 GB

See how fast it feels

With memory offload — actual speed may be lower
See how fast it feelsCodeLlama 7B Instruct on Radeon Pro W7500 8GB
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: 7.4 tok/s decode · 26.2s TTFT (warm) · 19 tok/s prefill

What limits this setup

Usable VRAM is the main blocker for this model.

Not enough usable memory

The model needs 13.8 GB, but this setup only exposes 8.0 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

WorkloadGradeFitDecodeTTFTContext
ChatFToo heavy14.9 tok/s7088 ms4K
CodingFToo heavy7.4 tok/s26209 ms4K
Agentic CodingFToo heavy4.6 tok/s60655 ms4K
ReasoningFToo heavy7.4 tok/s30974 ms4K
RAGFToo heavy4.6 tok/s75819 ms4K

Quantization options

How CodeLlama 7B Instruct (7B params) fits at each quantization level on Radeon Pro W7500 8GB (8.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
2.7 GB
LowA77
Q3_K_S
3
3.4 GB
LowA77
NVFP4
4
3.9 GB
MediumA77
Q4_K_M
4
4.3 GB
MediumA76
Q5_K_MBest for your GPU
5
5.0 GB
HighA76
Q6_K
6
5.7 GB
HighF0
Q8_0
8
7.5 GB
Very HighF0
F16
16
14.3 GB
MaximumF0

Upgrade options

Hardware that runs CodeLlama 7B Instruct well

RX 7600 XT 16GBBudget pick
16 GB VRAM (+8)288 GB/s (+64)
A
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 (+8)320 GB/s (+96)
A
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 (+4)432 GB/s (+208)
B
Makes the model fit on the accelerator instead of staying completely out of reach.32 tok/s decode

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

Raises estimated decode speed by about 332%.

~$449 MSRP

RX 7900 XT 20GBBiggest leap
20 GB VRAM (+12)800 GB/s (+576)
A
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

See all results for Radeon Pro W7500 8GBSee all hardware for CodeLlama 7B Instruct