12 GB VRAM (+4)360 GB/s (+136)
C
Raises estimated decode speed by about 61%.48.7 tok/s decode
Raises estimated decode speed by about 61%.
Adds memory headroom for longer context windows and future model growth.
~$329 MSRP
Can granite 8b code instruct 4k run on RTX 3050 8GB?
YES — With Offload
C50Usable
○Estimated from fit model
granite 8b code instruct 4k needs ~7.8 GB VRAM. RTX 3050 8GB has 8.0 GB. With Q4_K_M quantization, expect ~30 tok/s.
Runtime: OllamaCapacity: OffloadBandwidth: Very lowStack: BasicBottleneck: Memory bandwidth
Share:
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, 30.3 tok/s, Runs with offload
7.8 GB required8.0 GB available
98% VRAM used
Fit status
Runs with offload
Decode
30.3 tok/s
TTFT
6390 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 3050 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: 30.3 tok/s decode · 6.4s TTFT (warm) · 76 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 | 30.3 tok/s | 3486 ms | 19K |
| Coding | C | Runs with offload | 30.3 tok/s | 6390 ms | 19K |
| Agentic Coding | D | Very compromised (needs ~0.4 GB host RAM) | 18.8 tok/s | 14985 ms | 19K |
| Reasoning | C | Runs with offload | 30.3 tok/s | 7552 ms | 19K |
| RAG | D | Very compromised (needs ~0.4 GB host RAM) | 18.8 tok/s | 18731 ms |
Quantization options
How granite 8b code instruct 4k (8B params) fits at each quantization level on RTX 3050 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 |
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 startUpgrade options
Hardware that runs granite 8b code instruct 4k well
16 GB VRAM (+8)448 GB/s (+224)
C
Raises estimated decode speed by about 88%.56.9 tok/s decode
Raises estimated decode speed by about 88%.
Adds memory headroom for longer context windows and future model growth.
~$449 MSRP
16 GB VRAM (+8)288 GB/s (+64)
C
Raises estimated decode speed by about 42%.43.1 tok/s decode
Raises estimated decode speed by about 42%.
Adds memory headroom for longer context windows and future model growth.
~$499 MSRP