RX 7700 XT 12GBコスパ重視
12 GB VRAM (+4)432 GB/s (+144)
C
Raises estimated decode speed by about 53%.60.7 tok/sデコード
Raises estimated decode speed by about 53%.
Adds memory headroom for longer context windows and future model growth.
〜$449 MSRP
Can starcoder2 7b run on Radeon RX 7600M 8GB?
YES — Tight Fit
C51Usable
Estimated from fit model
starcoder2 7b needs ~6.8 GB VRAM. Radeon RX 7600M 8GB has 8.0 GB. With Q4_K_M quantization, expect ~40 tok/s.
Runtime: llama.cppCapacity: TightBandwidth: LowStack: 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.
Select quantization to explore
Q4_K_M (Medium quality) — 6.8 GB, 39.8 tok/s, Tight fit
6.8 GB required8.0 GB available
85% VRAM used
Fit status
Tight fit
Decode
39.8 tok/s
TTFT
4865 ms
Safe context
40K
Memory
6.8 GB / 8.0 GB
Memory breakdown
Weights4.3 GB
KV Cache0.8 GB
Runtime0.9 GB
Headroom0.8 GB
See how fast it feels
See how fast it feelsstarcoder2 7b on Radeon RX 7600M 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: 39.8 tok/s decode · 4.9s TTFT (warm) · 100 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 | C | Runs well | 39.8 tok/s | 2654 ms | 40K |
| Coding | C | Tight fit | 39.8 tok/s | 4865 ms | 40K |
| Agentic Coding | C | Runs with offload | 39.8 tok/s | 7076 ms | 40K |
| Reasoning | C | Tight fit | 39.8 tok/s | 5750 ms | 40K |
| RAG | C | Runs with offload | 39.8 tok/s | 8846 ms | 40K |
Quantization options
How starcoder2 7b (7B params) fits at each quantization level on Radeon RX 7600M 8GB (8.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 2.7 GB | Low | C53 |
Q3_K_S | 3 | 3.4 GB | Low | C53 |
NVFP4 | 4 | 3.9 GB | Medium | C53 |
Q4_K_M | 4 | 4.3 GB | Medium | C53 |
Q5_K_MBest for your GPU | 5 | 5.0 GB | High | C52 |
Q6_K | 6 | 5.7 GB | High | F0 |
Q8_0 | 8 | 7.5 GB | Very High | F0 |
F16 | 16 | 14.3 GB | Maximum | F0 |
Get started
Copy-paste commands to run starcoder2 7b on your machine.
Run
lms load hf-quantfactory--starcoder2-7b-gguf && lms server startアップグレードオプション
starcoder2 7bを快適に動かすハードウェア
RX 6700 XT 12GBコスパ最良
12 GB VRAM (+4)384 GB/s (+96)
C
Adds memory headroom for longer context windows and future model growth.46.8 tok/sデコード
Adds memory headroom for longer context windows and future model growth.
〜$479 MSRP
RX 9070 16GBAMDアップグレード
16 GB VRAM (+8)640 GB/s (+352)
C
Raises estimated decode speed by about 133%.92.9 tok/sデコード
Raises estimated decode speed by about 133%.
Adds memory headroom for longer context windows and future model growth.
〜$479 MSRP
Frequently asked questions
Can Radeon RX 7600M 8GB run starcoder2 7b?
Yes, Radeon RX 7600M 8GB can run starcoder2 7b with a C grade (Tight fit). Expected decode speed: 39.8 tok/s.
How much VRAM does starcoder2 7b need?
starcoder2 7b (7B parameters) requires approximately 6.8 GB of memory with Q4_K_M quantization.
What is the best quantization for starcoder2 7b?
The recommended quantization for starcoder2 7b is Q4_K_M, which balances quality and memory efficiency.
What speed will starcoder2 7b run at on Radeon RX 7600M 8GB?
On Radeon RX 7600M 8GB, starcoder2 7b achieves approximately 39.8 tokens per second decode speed with a time-to-first-token of 4865ms using Q4_K_M quantization.
Can Radeon RX 7600M 8GB run starcoder2 7b for coding?
For coding workloads, starcoder2 7b on Radeon RX 7600M 8GB receives a C grade with 39.8 tok/s and 40K context.
What context window can starcoder2 7b use on Radeon RX 7600M 8GB?
On Radeon RX 7600M 8GB, starcoder2 7b can safely use up to 40K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
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