RTX 3050 8GBコスパ重視8 GB VRAM (+2)
A
Adds memory headroom for longer context windows and future model growth.42 tok/sデコード
Adds memory headroom for longer context windows and future model growth.
〜$249 MSRP
Can Granite Code 3B run on GTX 1660 Super 6GB?
YES — With Offload
B67Good
Estimated from fit model
Granite Code 3B needs ~6.1 GB VRAM. GTX 1660 Super 6GB has 6.0 GB. With Q4_K_M quantization, expect ~42 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) — 6.1 GB, 42.0 tok/s, Runs with offload (needs ~0 GB host RAM)
6.1 GB required6.0 GB available
102% VRAM needed
100 MB over capacity — needs offload or smaller quantization
Fit status
Runs with offload (needs ~0 GB host RAM)
Decode
42.0 tok/s
TTFT
4610 ms
Safe context
8K
Memory
6.1 GB / 6.0 GB
Memory breakdown
Weights1.8 GB
KV Cache2.5 GB
Runtime1.2 GB
Headroom0.6 GB
See how fast it feels
See how fast it feelsGranite Code 3B on GTX 1660 Super 6GB
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: 42.0 tok/s decode · 4.6s TTFT (warm) · 105 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 | A | Runs well | 42.0 tok/s | 2514 ms | 8K |
| Coding | B | Runs with offload (needs ~0 GB host RAM) | 42.0 tok/s | 4610 ms | 8K |
| Agentic Coding | F | Too heavy | 33.6 tok/s | 8372 ms | 8K |
| Reasoning | B | Runs with offload (needs ~0 GB host RAM) | 42.0 tok/s | 5448 ms | 8K |
| RAG | F | Too heavy | 33.6 tok/s | 10465 ms | 8K |
Quantization options
How Granite Code 3B (3B params) fits at each quantization level on GTX 1660 Super 6GB (6.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 1.2 GB | Low | B69 |
Q3_K_S | 3 | 1.5 GB | Low | A70 |
NVFP4 | 4 | 1.7 GB | Medium | A71 |
Q4_K_M | 4 | 1.8 GB | Medium | A71 |
Q5_K_M | 5 | 2.2 GB | High | A71 |
Q6_K | 6 | 2.5 GB | High | A70 |
Q8_0Best for your GPU | 8 | 3.2 GB | Very High | B70 |
F16 | 16 | 6.1 GB | Maximum | F0 |
Get started
Copy-paste commands to run Granite Code 3B on your machine.
Run
ollama run granite-code:3bアップグレードオプション
Granite Code 3Bを快適に動かすハードウェア
RTX 3050 8GBコスパ重視RTX 4060 8GBコスパ最良8 GB VRAM (+2)
A
Adds memory headroom for longer context windows and future model growth.42 tok/sデコード
Adds memory headroom for longer context windows and future model growth.
〜$299 MSRP
RTX 5060 8GBNVIDIAアップグレード8 GB VRAM (+2)448 GB/s (+112)
A
Adds memory headroom for longer context windows and future model growth.42 tok/sデコード
Adds memory headroom for longer context windows and future model growth.
〜$299 MSRP
Frequently asked questions
Can GTX 1660 Super 6GB run Granite Code 3B?
Yes, GTX 1660 Super 6GB can run Granite Code 3B with a B grade (Runs with offload (needs ~0 GB host RAM)). Expected decode speed: 42.0 tok/s.
How much VRAM does Granite Code 3B need?
Granite Code 3B (3B parameters) requires approximately 6.1 GB of memory with Q4_K_M quantization.
What is the best quantization for Granite Code 3B?
The recommended quantization for Granite Code 3B is Q4_K_M, which balances quality and memory efficiency.
What speed will Granite Code 3B run at on GTX 1660 Super 6GB?
On GTX 1660 Super 6GB, Granite Code 3B achieves approximately 42.0 tokens per second decode speed with a time-to-first-token of 4610ms using Q4_K_M quantization.
Can GTX 1660 Super 6GB run Granite Code 3B for coding?
For coding workloads, Granite Code 3B on GTX 1660 Super 6GB receives a B grade with 42.0 tok/s and 8K context.
What context window can Granite Code 3B use on GTX 1660 Super 6GB?
On GTX 1660 Super 6GB, Granite Code 3B can safely use up to 8K tokens of context. The model's official context limit is 8K, but available memory constrains the safe maximum.
What should I upgrade first if Granite Code 3B feels slow on GTX 1660 Super 6GB?
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/granite-code-3b-on-gtx-1660-super-6gb" 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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