Can Codestral RAG 19B Pruned i1 run on Radeon RX 7700S 8GB?

NO — Won't Fit

F0Won't run

○Estimated from fit model

Codestral RAG 19B Pruned i1 needs ~15.5 GB but Radeon RX 7700S 8GB only has 8.0 GB. Try a smaller quantization or lighter model.

Runtime: llama.cppCapacity: No fitBandwidth: LowStack: StandardBottleneck: Memory capacity

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) — 15.5 GB, exceeds 8.0 GB available

15.5 GB required8.0 GB available

194% VRAM needed

7.5 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

2.7 tok/s

TTFT

71007 ms

Safe context

Memory

15.5 GB / 8.0 GB

Offload

50%

Memory breakdown

Weights11.6 GB

KV Cache2.2 GB

Runtime0.9 GB

Headroom0.8 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsCodestral RAG 19B Pruned i1 on Radeon RX 7700S 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: 2.7 tok/s decode · 71.0s TTFT (warm) · 7 tok/s prefill

What limits this setup

Usable VRAM is the main blocker for this model.

Not enough usable memory

The model needs 15.5 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

Workload	Grade	Fit	Decode	TTFT	Context
Chat	F	Too heavy	3.2 tok/s	33113 ms	4K
Coding	F	Too heavy	2.7 tok/s	71007 ms	4K
Agentic Coding	F	Too heavy	2.2 tok/s	128050 ms	4K
Reasoning	F	Too heavy	2.7 tok/s	83918 ms	4K
RAG	F	Too heavy	2.2 tok/s	160063 ms	4K

Quantization options

How Codestral RAG 19B Pruned i1 (19B params) fits at each quantization level on Radeon RX 7700S 8GB (8.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	7.4 GB	Low	F0
Q3_K_S	3	9.3 GB	Low	F0
NVFP4	4

Upgrade options

Hardware that runs Codestral RAG 19B Pruned i1 well

RX 7600 XT 16GBBudget pick

16 GB VRAM (+8)

Makes the model fit on the accelerator instead of staying completely out of reach.10.4 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 (+32)

Makes the model fit on the accelerator instead of staying completely out of reach.12.5 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 9070 16GBAMD upgrade

16 GB VRAM (+8)640 GB/s (+352)

Makes the model fit on the accelerator instead of staying completely out of reach.25.2 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.

~$479 MSRP

👁 NVIDIA
RTX 5090 Laptop 24GBBiggest leap

24 GB VRAM (+16)896 GB/s (+608)

Makes the model fit on the accelerator instead of staying completely out of reach.64.9 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.

Frequently asked questions

See all results for Radeon RX 7700S 8GB See all hardware for Codestral RAG 19B Pruned i1

URL: https://willitrunai.com/can-run/hf-mradermacher--codestral-rag-19b-pruned-i1-gguf-on-rx-7700s-8gb

⇱

Can Codestral RAG 19B Pruned i1 run on Radeon RX 7700S 8GB?

NO — Won't Fit

Choose the run profile you care about

Memory breakdown

See how fast it feels

What limits this setup

Best improvement path

Performance by workload

Quantization options

Hardware that runs Codestral RAG 19B Pruned i1 well

Frequently asked questions