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

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
👁 Alibaba Qwen 3.5 9B	9B	S	77.7 tok/s
👁 Alibaba Qwen 3 14B	14B	S	50.2 tok/s
👁 Alibaba Qwen 3 8B	8B	S	87.4 tok/s
👁 Microsoft Phi-4-reasoning-plus 14B	14.7B	S	47.6 tok/s
👁 OpenAI GPT-OSS 20B	21B	A	47.1 tok/s

Frequently asked questions

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

URL: https://willitrunai.com/can-run/codellama-7b-instruct-on-rx-9070-16gb

⇱ CodeLlama 7B Instruct on RX 9070 16GB? TIGHT FIT