Can Granite Code 8B run on RTX 3070 8GB?

BARELY — Tight on Memory

B66Good

Estimated from fit model

Granite Code 8B needs ~8.8 GB VRAM. RTX 3070 8GB has 8.0 GB. With Q4_K_M quantization, expect ~42 tok/s.

Runtime: OllamaCapacity: OffloadBandwidth: LowStack: BasicBottleneck: Host offload

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) — 8.8 GB, 42.1 tok/s, Very compromised (needs ~0.5 GB host RAM)

8.8 GB required8.0 GB available

110% VRAM needed

0.8 GB over capacity — needs offload or smaller quantization

Fit status

Very compromised (needs ~0.5 GB host RAM)

Decode

42.1 tok/s

TTFT

4603 ms

Safe context

Memory

8.8 GB / 8.0 GB

Offload

10%

Memory breakdown

Weights4.9 GB

KV Cache2.0 GB

Runtime1.2 GB

Headroom0.8 GB

See how fast it feels

See how fast it feelsGranite Code 8B 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: 42.1 tok/s decode · 4.6s TTFT (warm) · 105 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.

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 0.5 GB of extra host RAM just for the offloaded portion, before OS and other tools.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	A	Runs with offload	69.1 tok/s	1529 ms	8K
Coding	B	Very compromised (needs ~0.5 GB host RAM)	42.1 tok/s	4603 ms	8K
Agentic Coding	F	Too heavy	27.6 tok/s	10195 ms	8K
Reasoning	B	Very compromised (needs ~0.5 GB host RAM)	42.1 tok/s	5440 ms	8K
RAG	F	Too heavy	27.6 tok/s	12744 ms	8K

Quantization options

How Granite Code 8B (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	A79
Q3_K_S	3	3.9 GB	Low	A78
NVFP4	4	4.5 GB	Medium	A78
Q4_K_MBest for your GPU	4	4.9 GB	Medium	A78
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 Code 8B on your machine.

Run

ollama run granite-code:8b

Upgrade options

Hardware that runs Granite Code 8B well

RTX 3060 12GBBudget pick

12 GB VRAM (+4)

Removes host-memory offload, which is usually the single biggest latency and throughput win.52.3 tok/s decode

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

Adds memory headroom for longer context windows and future model growth.

~$329 MSRP

RTX 5060 Ti 16GBBest value

16 GB VRAM (+8)

Removes host-memory offload, which is usually the single biggest latency and throughput win.61.2 tok/s decode

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

Raises estimated decode speed by about 45%.

~$449 MSRP

RTX 4060 Ti 16GBNVIDIA upgrade

16 GB VRAM (+8)

Removes host-memory offload, which is usually the single biggest latency and throughput win.46.3 tok/s decode

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

Adds memory headroom for longer context windows and future model growth.

~$499 MSRP

Frequently asked questions

Can RTX 3070 8GB run Granite Code 8B?

Yes, RTX 3070 8GB can run Granite Code 8B with a B grade (Very compromised (needs ~0.5 GB host RAM)). Expected decode speed: 42.1 tok/s.

How much VRAM does Granite Code 8B need?

Granite Code 8B (8B parameters) requires approximately 8.8 GB of memory with Q4_K_M quantization.

What is the best quantization for Granite Code 8B?

The recommended quantization for Granite Code 8B is Q4_K_M, which balances quality and memory efficiency.

What speed will Granite Code 8B run at on RTX 3070 8GB?

On RTX 3070 8GB, Granite Code 8B achieves approximately 42.1 tokens per second decode speed with a time-to-first-token of 4603ms using Q4_K_M quantization.

Can RTX 3070 8GB run Granite Code 8B for coding?

For coding workloads, Granite Code 8B on RTX 3070 8GB receives a B grade with 42.1 tok/s and 8K context.

What context window can Granite Code 8B use on RTX 3070 8GB?

On RTX 3070 8GB, Granite Code 8B 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 8B feels slow on RTX 3070 8GB?

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.

See all results for RTX 3070 8GB See all hardware for Granite Code 8B

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Preview:

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.

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 0.5 GB of extra host RAM just for the offloaded portion, before OS and other tools.

Workload

Grade

Fit

Decode

TTFT

Context

Chat

Runs with offload

69.1 tok/s

1529 ms

Coding

Very compromised (needs ~0.5 GB host RAM)

42.1 tok/s

4603 ms

Agentic Coding

Too heavy

27.6 tok/s

10195 ms

Reasoning

Very compromised (needs ~0.5 GB host RAM)

42.1 tok/s

5440 ms

RAG

Too heavy

27.6 tok/s

12744 ms

Quant

Bits

VRAM

Quality

Fit

Q2_K

3.1 GB

Low

A79

Q3_K_S

3.9 GB

Low

A78

NVFP4

4.5 GB

Medium

A78

Q4_K_MBest for your GPU

4.9 GB

Medium

A78

Q5_K_M

5.8 GB

High

Q6_K

6.6 GB

High

Q8_0

8.6 GB

Very High

F16

16.4 GB

Maximum

Frequently asked questions

Can RTX 3070 8GB run Granite Code 8B?

Yes, RTX 3070 8GB can run Granite Code 8B with a B grade (Very compromised (needs ~0.5 GB host RAM)). Expected decode speed: 42.1 tok/s.

How much VRAM does Granite Code 8B need?

Granite Code 8B (8B parameters) requires approximately 8.8 GB of memory with Q4_K_M quantization.

What is the best quantization for Granite Code 8B?

The recommended quantization for Granite Code 8B is Q4_K_M, which balances quality and memory efficiency.

What speed will Granite Code 8B run at on RTX 3070 8GB?

On RTX 3070 8GB, Granite Code 8B achieves approximately 42.1 tokens per second decode speed with a time-to-first-token of 4603ms using Q4_K_M quantization.

Can RTX 3070 8GB run Granite Code 8B for coding?

For coding workloads, Granite Code 8B on RTX 3070 8GB receives a B grade with 42.1 tok/s and 8K context.

What context window can Granite Code 8B use on RTX 3070 8GB?

On RTX 3070 8GB, Granite Code 8B 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 8B feels slow on RTX 3070 8GB?

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.