RTX 3060 12GBOpción económica12 GB VRAM (+4)
C
Añade margen de memoria para más contexto y para que el modelo envejezca mejor.48.7 tok/s decodificación
Añade margen de memoria para más contexto y para que el modelo envejezca mejor.
~$329 MSRP
Can granite 8b code instruct 4k run on RTX 3070 8GB?
YES — With Offload
C53Usable
Estimated from fit model
granite 8b code instruct 4k needs ~7.8 GB VRAM. RTX 3070 8GB has 8.0 GB. With Q4_K_M quantization, expect ~64 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, 64.3 tok/s, Runs with offload
7.8 GB required8.0 GB available
98% VRAM used
Fit status
Runs with offload
Decode
64.3 tok/s
TTFT
3013 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 3070 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: 64.3 tok/s decode · 3.0s TTFT (warm) · 161 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 | 64.3 tok/s | 1643 ms | 19K |
| Coding | C | Runs with offload | 64.3 tok/s | 3013 ms | 19K |
| Agentic Coding | C | Very compromised (needs ~0.4 GB host RAM) | 39.9 tok/s | 7064 ms | 19K |
| Reasoning | C | Runs with offload | 64.3 tok/s | 3560 ms | 19K |
| RAG | C | Very compromised (needs ~0.4 GB host RAM) | 39.9 tok/s | 8830 ms | 19K |
Quantization options
How granite 8b code instruct 4k (8B params) fits at each quantization level on RTX 3070 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 startOpciones de mejora
Hardware que ejecuta bien granite 8b code instruct 4k
RTX 3060 12GBOpción económicaRTX 5070 12GBMejor relación calidad-precio12 GB VRAM (+4)672 GB/s (+224)
B
Sube la velocidad estimada de decodificación alrededor de un 35%.86.8 tok/s decodificación
Sube la velocidad estimada de decodificación alrededor de un 35%.
Añade margen de memoria para más contexto y para que el modelo envejezca mejor.
~$549 MSRP
RTX 4070 Super 12GBMejora NVIDIA12 GB VRAM (+4)504 GB/s (+56)
B
Añade margen de memoria para más contexto y para que el modelo envejezca mejor.79.5 tok/s decodificación
Añade margen de memoria para más contexto y para que el modelo envejezca mejor.
~$599 MSRP
Frequently asked questions
Can RTX 3070 8GB run granite 8b code instruct 4k?
Yes, RTX 3070 8GB can run granite 8b code instruct 4k with a C grade (Runs with offload). Expected decode speed: 64.3 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 3070 8GB?
On RTX 3070 8GB, granite 8b code instruct 4k achieves approximately 64.3 tokens per second decode speed with a time-to-first-token of 3013ms using Q4_K_M quantization.
Can RTX 3070 8GB run granite 8b code instruct 4k for coding?
For coding workloads, granite 8b code instruct 4k on RTX 3070 8GB receives a C grade with 64.3 tok/s and 19K context.
What context window can granite 8b code instruct 4k use on RTX 3070 8GB?
On RTX 3070 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 3070 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-3070-8gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview: