RTX 3060 12GBOpção econômica12 GB VRAM (+4)
C
Adds memory headroom for longer context windows and future model growth.48.7 tok/s decodificação
Adds memory headroom for longer context windows and future model growth.
~$329 MSRP
Can granite 8b code instruct 4k run on RTX 5060 8GB?
YES — With Offload
C52Usable
Estimated from fit model
granite 8b code instruct 4k needs ~7.8 GB VRAM. RTX 5060 8GB has 8.0 GB. With Q4_K_M quantization, expect ~56 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, 56.0 tok/s, Runs with offload
7.8 GB required8.0 GB available
98% VRAM used
Fit status
Runs with offload
Decode
56.0 tok/s
TTFT
3457 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 5060 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: 56.0 tok/s decode · 3.5s TTFT (warm) · 140 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 | 56.0 tok/s | 1886 ms | 19K |
| Coding | C | Runs with offload | 56.0 tok/s | 3457 ms | 19K |
| Agentic Coding | C | Very compromised (needs ~0.4 GB host RAM) | 35.6 tok/s | 7904 ms | 19K |
| Reasoning | C | Runs with offload | 56.0 tok/s | 4086 ms | 19K |
| RAG | C | Very compromised (needs ~0.4 GB host RAM) | 35.6 tok/s | 9880 ms | 19K |
Quantization options
How granite 8b code instruct 4k (8B params) fits at each quantization level on RTX 5060 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 startOpções de upgrade
Hardware que roda bem granite 8b code instruct 4k
RTX 3060 12GBOpção econômicaRTX 5070 12GBMelhor custo-benefício12 GB VRAM (+4)672 GB/s (+224)
B
Raises estimated decode speed by about 55%.86.8 tok/s decodificação
Raises estimated decode speed by about 55%.
Adds memory headroom for longer context windows and future model growth.
~$549 MSRP
RTX 4070 Super 12GBUpgrade NVIDIA12 GB VRAM (+4)504 GB/s (+56)
B
Raises estimated decode speed by about 42%.79.5 tok/s decodificação
Raises estimated decode speed by about 42%.
Adds memory headroom for longer context windows and future model growth.
~$599 MSRP
Frequently asked questions
Can RTX 5060 8GB run granite 8b code instruct 4k?
Yes, RTX 5060 8GB can run granite 8b code instruct 4k with a C grade (Runs with offload). Expected decode speed: 56.0 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 5060 8GB?
On RTX 5060 8GB, granite 8b code instruct 4k achieves approximately 56.0 tokens per second decode speed with a time-to-first-token of 3457ms using Q4_K_M quantization.
Can RTX 5060 8GB run granite 8b code instruct 4k for coding?
For coding workloads, granite 8b code instruct 4k on RTX 5060 8GB receives a C grade with 56.0 tok/s and 19K context.
What context window can granite 8b code instruct 4k use on RTX 5060 8GB?
On RTX 5060 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 5060 8GB?
Buy headroom, not only minimum fit. A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
Embed this result▼
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/hf-ibm-granite--granite-8b-code-instruct-4k-gguf-on-rtx-5060-8gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview: