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URL: https://willitrunai.com/can-run/hf-ibm-granite--granite-8b-code-instruct-4k-gguf-on-gtx-1060-6gb

⇱ granite 8b code instruct 4k on GTX 1060 6GB? No — Alternati…

Can granite 8b code instruct 4k run on GTX 1060 6GB?

YES — With Q3_K_S

D38Poor
○Estimated from fit model

granite 8b code instruct 4k needs ~6.7 GB VRAM. GTX 1060 6GB has 6.0 GB. With Q3_K_S quantization, expect ~16 tok/s.

Runtime: OllamaCapacity: OffloadBandwidth: Very lowStack: BasicBottleneck: Host offload
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.

granite 8b code instruct 4k at Q4_K_M needs 7.6 GB — too much for GTX 1060 6GB (6.0 GB). Runs at Q3_K_S (6.7 GB) with low quality. 2 quantization levels fit.
Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 7.6 GB, exceeds 6.0 GB available
7.6 GB required6.0 GB available
127% VRAM needed

1.6 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

10.0 tok/s

TTFT

19313 ms

Safe context

4K

Memory

7.6 GB / 6.0 GB

Offload

20%

Memory breakdown

Weights4.9 GB
KV Cache0.9 GB
Runtime1.2 GB
Headroom0.6 GB

See how fast it feels

With memory offload — actual speed may be lower
See how fast it feelsgranite 8b code instruct 4k on GTX 1060 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: 10.0 tok/s decode · 19.3s TTFT (warm) · 25 tok/s prefill

What limits this setup

It fits through host-memory offload, and offload is the main reason performance drops.

CPU or host-memory offload is active

About 10% of the working set spills out of accelerator memory, which usually hurts latency and sustained decode throughput.

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

Remove offload with more accelerator memory

Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.

Buy headroom, not only minimum fit

A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

Increase host RAM if you keep offloading

This setup may need roughly 0.4 GB of extra host RAM just for the offloaded portion, before OS and other tools.

Performance by workload

WorkloadGradeFitDecodeTTFTContext
ChatDVery compromised (needs ~0.8 GB host RAM)11.5 tok/s9163 ms4K
CodingFToo heavy10.0 tok/s19313 ms4K
Agentic CodingFToo heavy7.8 tok/s36246 ms4K
ReasoningFToo heavy10.0 tok/s22824 ms4K
RAGFToo heavy7.8 tok/s45308 ms4K

Quantization options

How granite 8b code instruct 4k (8B params) fits at each quantization level on GTX 1060 6GB (6.0 GB usable).

QuantBitsVRAMQualityFit
Q2_KBest for your GPU
2
3.1 GB
LowC54
Q3_K_S
3
3.9 GB
LowF0
NVFP4
4
4.5 GB
MediumF0
Q4_K_M
4
4.9 GB
MediumF0
Q5_K_M
5
5.8 GB
HighF0
Q6_K
6
6.6 GB
HighF0
Q8_0
8
8.6 GB
Very HighF0
F16
16
16.4 GB
MaximumF0

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

Upgrade options

Hardware that runs granite 8b code instruct 4k well

👁 NVIDIA
RTX 3050 8GBBudget pick
8 GB VRAM (+2)224 GB/s (+32)
C
Makes the model fit on the accelerator instead of staying completely out of reach.30.3 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$249 MSRP

👁 NVIDIA
RTX 5060 8GBBest value
8 GB VRAM (+2)448 GB/s (+256)
C
Makes the model fit on the accelerator instead of staying completely out of reach.56 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$299 MSRP

👁 NVIDIA
RTX 4060 8GBNVIDIA upgrade
8 GB VRAM (+2)272 GB/s (+80)
C
Makes the model fit on the accelerator instead of staying completely out of reach.40.7 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$299 MSRP

Frequently asked questions

See all results for GTX 1060 6GBSee all hardware for granite 8b code instruct 4k