RX 7600 XT 16GBコスパ重視
16 GB VRAM (+8)288 GB/s (+32)
C
Raises estimated decode speed by about 51%.34.2 tok/sデコード
Raises estimated decode speed by about 51%.
Adds memory headroom for longer context windows and future model growth.
〜$329 MSRP
Can granite 8b code instruct 4k run on RX 590 8GB?
YES — Tight Fit
C49Usable
Estimated from fit model
granite 8b code instruct 4k needs ~7.5 GB VRAM. RX 590 8GB has 8.0 GB. With Q4_K_M quantization, expect ~23 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) — 7.5 GB, 22.6 tok/s, Tight fit
7.5 GB required8.0 GB available
94% VRAM used
Fit status
Tight fit
Decode
22.6 tok/s
TTFT
8583 ms
Safe context
24K
Memory
7.5 GB / 8.0 GB
Memory breakdown
Weights4.9 GB
KV Cache0.9 GB
Runtime0.9 GB
Headroom0.8 GB
See how fast it feels
See how fast it feelsgranite 8b code instruct 4k on RX 590 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: 22.6 tok/s decode · 8.6s TTFT (warm) · 56 tok/s prefill
What limits this setup
This setup is broadly balanced for this model.
Very little memory headroom
You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.
Older PCIe generation
PCIe 3.0 is workable, but it compounds the penalty when you offload heavily or try to scale across multiple cards.
Best improvement path
Buy headroom, not only minimum fit
A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
Performance by workload
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | C | Tight fit | 22.6 tok/s | 4681 ms | 24K |
| Coding | C | Tight fit | 22.6 tok/s | 8583 ms | 24K |
| Agentic Coding | D | Runs with offload (needs ~0.3 GB host RAM) | 14.6 tok/s | 19325 ms | 24K |
| Reasoning | C | Tight fit | 22.6 tok/s | 10143 ms | 24K |
| RAG | D | Runs with offload (needs ~0.3 GB host RAM) | 14.6 tok/s | 24157 ms | 24K |
Quantization options
How granite 8b code instruct 4k (8B params) fits at each quantization level on RX 590 8GB (8.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 3.1 GB | Low | C54 |
Q3_K_S | 3 | 3.9 GB | Low | C53 |
NVFP4 | 4 | 4.5 GB | Medium | C53 |
Q4_K_MBest for your GPU | 4 | 4.9 GB | Medium | C53 |
Q5_K_M | 5 | 5.8 GB | High | F0 |
Q6_K | 6 | 6.6 GB | High | F0 |
Q8_0 | 8 | 8.6 GB | Very High | F0 |
F16 | 16 | 16.4 GB | Maximum | F0 |
Get started
Copy-paste commands to run granite 8b code instruct 4k on your machine.
Run
lms load hf-ibm-granite--granite-8b-code-instruct-4k-gguf && lms server startアップグレードオプション
granite 8b code instruct 4kを快適に動かすハードウェア
RX 9060 XT 16GBコスパ最良
16 GB VRAM (+8)320 GB/s (+64)
C
Raises estimated decode speed by about 83%.41.3 tok/sデコード
Raises estimated decode speed by about 83%.
Adds memory headroom for longer context windows and future model growth.
〜$349 MSRP
RX 7700 XT 12GBAMDアップグレード
12 GB VRAM (+4)432 GB/s (+176)
C
Raises estimated decode speed by about 135%.53.1 tok/sデコード
Raises estimated decode speed by about 135%.
Adds memory headroom for longer context windows and future model growth.
〜$449 MSRP
Frequently asked questions
Can RX 590 8GB run granite 8b code instruct 4k?
Yes, RX 590 8GB can run granite 8b code instruct 4k with a C grade (Tight fit). Expected decode speed: 22.6 tok/s.
How much VRAM does granite 8b code instruct 4k need?
granite 8b code instruct 4k (8B parameters) requires approximately 7.5 GB of memory with Q4_K_M quantization.
What is the best quantization for granite 8b code instruct 4k?
The recommended quantization for granite 8b code instruct 4k is Q4_K_M, which balances quality and memory efficiency.
What speed will granite 8b code instruct 4k run at on RX 590 8GB?
On RX 590 8GB, granite 8b code instruct 4k achieves approximately 22.6 tokens per second decode speed with a time-to-first-token of 8583ms using Q4_K_M quantization.
Can RX 590 8GB run granite 8b code instruct 4k for coding?
For coding workloads, granite 8b code instruct 4k on RX 590 8GB receives a C grade with 22.6 tok/s and 24K context.
What context window can granite 8b code instruct 4k use on RX 590 8GB?
On RX 590 8GB, granite 8b code instruct 4k can safely use up to 24K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
What should I upgrade first if granite 8b code instruct 4k feels slow on RX 590 8GB?
Buy headroom, not only minimum fit. A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
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