RTX 3060 12GBコスパ重視12 GB VRAM (+4)360 GB/s (+88)
C
Adds memory headroom for longer context windows and future model growth.48.7 tok/sデコード
Adds memory headroom for longer context windows and future model growth.
〜$329 MSRP
Can granite 8b code instruct 4k run on RTX 4060 8GB?
YES — With Offload
C51Usable
Estimated from fit model
granite 8b code instruct 4k needs ~7.8 GB VRAM. RTX 4060 8GB has 8.0 GB. With Q4_K_M quantization, expect ~41 tok/s.
Runtime: OllamaCapacity: OffloadBandwidth: LowStack: BasicBottleneck: 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) — 7.8 GB, 40.7 tok/s, Runs with offload
7.8 GB required8.0 GB available
98% VRAM used
Fit status
Runs with offload
Decode
40.7 tok/s
TTFT
4758 ms
Safe context
19K
Memory
7.8 GB / 8.0 GB
Memory breakdown
Weights4.9 GB
KV Cache0.9 GB
Runtime1.2 GB
Headroom0.8 GB
See how fast it feels
See how fast it feelsgranite 8b code instruct 4k on RTX 4060 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: 40.7 tok/s decode · 4.8s TTFT (warm) · 102 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.
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 | 40.7 tok/s | 2595 ms | 19K |
| Coding | C | Runs with offload | 40.7 tok/s | 4758 ms | 19K |
| Agentic Coding | D | Very compromised (needs ~0.4 GB host RAM) | 25.2 tok/s | 11157 ms | 19K |
| Reasoning | C | Runs with offload | 40.7 tok/s | 5623 ms | 19K |
| RAG | D | Very compromised | 25.2 tok/s | 13946 ms | 19K |
Quantization options
How granite 8b code instruct 4k (8B params) fits at each quantization level on RTX 4060 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
RTX 3060 12GBコスパ重視RTX 5060 Ti 16GBコスパ最良16 GB VRAM (+8)448 GB/s (+176)
C
Raises estimated decode speed by about 40%.56.9 tok/sデコード
Raises estimated decode speed by about 40%.
Adds memory headroom for longer context windows and future model growth.
〜$449 MSRP
RTX 5070 12GBNVIDIAアップグレード12 GB VRAM (+4)672 GB/s (+400)
B
Raises estimated decode speed by about 113%.86.8 tok/sデコード
Raises estimated decode speed by about 113%.
Adds memory headroom for longer context windows and future model growth.
〜$549 MSRP
Frequently asked questions
Can RTX 4060 8GB run granite 8b code instruct 4k?
Yes, RTX 4060 8GB can run granite 8b code instruct 4k with a C grade (Runs with offload). Expected decode speed: 40.7 tok/s.
How much VRAM does granite 8b code instruct 4k need?
granite 8b code instruct 4k (8B parameters) requires approximately 7.8 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 RTX 4060 8GB?
On RTX 4060 8GB, granite 8b code instruct 4k achieves approximately 40.7 tokens per second decode speed with a time-to-first-token of 4758ms using Q4_K_M quantization.
Can RTX 4060 8GB run granite 8b code instruct 4k for coding?
For coding workloads, granite 8b code instruct 4k on RTX 4060 8GB receives a C grade with 40.7 tok/s and 19K context.
What context window can granite 8b code instruct 4k use on RTX 4060 8GB?
On RTX 4060 8GB, granite 8b code instruct 4k can safely use up to 19K 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 RTX 4060 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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<iframe src="https://willitrunai.com/embed/hf-ibm-granite--granite-8b-code-instruct-4k-gguf-on-rtx-4060-8gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
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