Can granite 8b code instruct 4k run on RTX 2070 8GB?

YES — With Offload

C52Usable
Estimated from fit model

granite 8b code instruct 4k needs ~7.8 GB VRAM. RTX 2070 8GB has 8.0 GB. With Q4_K_M quantization, expect ~55 tok/s.

Runtime: OllamaCapacity: OffloadBandwidth: LowStack: BasicBottleneck: Balanced
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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.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) 7.8 GB, 55.1 tok/s, Runs with offload
7.8 GB required8.0 GB available
98% VRAM used

Fit status

Runs with offload

Decode

55.1 tok/s

TTFT

3515 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 2070 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: 55.1 tok/s decode · 3.5s TTFT (warm) · 138 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

WorkloadGradeFitDecodeTTFTContext
ChatCTight fit55.1 tok/s1917 ms19K
CodingCRuns with offload55.1 tok/s3515 ms19K
Agentic CodingCVery compromised (needs ~0.4 GB host RAM)33.0 tok/s8544 ms19K
ReasoningCRuns with offload55.1 tok/s4154 ms19K
RAGCVery compromised (needs ~0.4 GB host RAM)33.0 tok/s10680 ms19K

Quantization options

How granite 8b code instruct 4k (8B params) fits at each quantization level on RTX 2070 8GB (8.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
3.1 GB
LowC54
Q3_K_S
3
3.9 GB
LowC53
NVFP4
4
4.5 GB
MediumC53
Q4_K_MBest for your GPU
4
4.9 GB
MediumC53
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

アップグレードオプション

granite 8b code instruct 4kを快適に動かすハードウェア

Frequently asked questions

Can RTX 2070 8GB run granite 8b code instruct 4k?

Yes, RTX 2070 8GB can run granite 8b code instruct 4k with a C grade (Runs with offload). Expected decode speed: 55.1 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 2070 8GB?

On RTX 2070 8GB, granite 8b code instruct 4k achieves approximately 55.1 tokens per second decode speed with a time-to-first-token of 3515ms using Q4_K_M quantization.

Can RTX 2070 8GB run granite 8b code instruct 4k for coding?

For coding workloads, granite 8b code instruct 4k on RTX 2070 8GB receives a C grade with 55.1 tok/s and 19K context.

What context window can granite 8b code instruct 4k use on RTX 2070 8GB?

On RTX 2070 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 2070 8GB?

Buy headroom, not only minimum fit. A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

See all results for RTX 2070 8GBSee all hardware for granite 8b code instruct 4k
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