Can Granite 3.1 8B run on RTX 4070 Ti Super 16GB?

YES — Runs Great

B59Good

Estimated from fit model

Granite 3.1 8B needs ~9.6 GB VRAM. RTX 4070 Ti Super 16GB has 16.0 GB. With Q4_K_M quantization, expect ~112 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: MediumStack: 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.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 9.6 GB, 112.0 tok/s, Runs well

9.6 GB required16.0 GB available

60% VRAM used

Fit status

Runs well

Decode

112.0 tok/s

TTFT

1729 ms

Safe context

68K

Memory

9.6 GB / 16.0 GB

Memory breakdown

Weights4.9 GB

KV Cache2.0 GB

Runtime1.2 GB

Headroom1.6 GB

See how fast it feels

See how fast it feelsGranite 3.1 8B on RTX 4070 Ti Super 16GB
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: 112.0 tok/s decode · 1.7s TTFT (warm) · 280 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

No major red flags

This recommendation has enough memory headroom and acceptable estimated speed for the selected workload.

Best improvement path

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	B	Runs well	112.0 tok/s	943 ms	68K
Coding	B	Runs well	112.0 tok/s	1729 ms	68K
Agentic Coding	B	Runs well	112.0 tok/s	2514 ms	68K
Reasoning	B	Runs well	112.0 tok/s	2043 ms	68K
RAG	B	Runs well	112.0 tok/s	3143 ms	68K

Inference speed

Granite 3.1 8B inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for Granite 3.1 8B at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~112 tok/s. Speed is memory-bandwidth bound, so cards that fit the whole model in VRAM run far faster than ones that offload to system RAM.

GPU / Mac	Memory	Quant	Speed (tok/s)	Fits?
RTX 5090 32GB	32 GB	Q4_K_M	112.0	Fits
RTX 4090 24GB	24 GB	Q4_K_M	112.0	Fits
RTX 4080 Super 16GB	16 GB	Q4_K_M	112.0	Fits
RTX 3090 24GB	24 GB	Q4_K_M	112.0	Fits
RX 7900 XTX 24GB	24 GB	Q4_K_M	112.0	Fits
Mac Studio M3 Ultra 256GB	256 GB	Q4_K_M	112.0	Fits
Mac Studio M2 Ultra 128GB	128 GB	Q4_K_M	112.0	Fits
Mac Studio M1 Ultra 128GB	128 GB	Q4_K_M	111.5	Fits
RTX 4070 12GB	12 GB	Q4_K_M	95.8	Fits
MacBook Pro M4 Max 128GB	128 GB	Q4_K_M	87.1	Fits
MacBook Pro M4 Max 64GB	64 GB	Q4_K_M	87.1	Fits
MacBook Pro M3 Max 64GB	64 GB	Q4_K_M	60.8	Fits
RTX 3060 12GB	12 GB	Q4_K_M	60.2	Fits
MacBook Pro M1 Max 64GB	64 GB	Q4_K_M	55.7	Fits
MacBook Pro M4 Pro 48GB	48 GB	Q4_K_M	53.3	Fits
RTX 4060 8GB	8 GB	Q4_K_M	32.9	Offloads

Estimates for single-stream decoding at Q4_K_M; real tokens/sec varies with prompt length, context, batch size, and runtime build. Prompt processing (prefill) is faster than the decode figures shown here.

Quantization options

How Granite 3.1 8B (8B params) fits at each quantization level on RTX 4070 Ti Super 16GB (16.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.1 GB	Low	C52
Q3_K_S	3	3.9 GB	Low	C52
NVFP4	4	4.5 GB	Medium	C53
Q4_K_M	4	4.9 GB	Medium	C53
Q5_K_M	5	5.8 GB	High	C54
Q6_K	6	6.6 GB	High	C55
Q8_0Best for your GPU	8	8.6 GB	Very High	B56
F16	16	16.4 GB	Maximum	F0

Get started

Copy-paste commands to run Granite 3.1 8B on your machine.

Run

ollama run granite3.1-dense

Frequently asked questions

Can RTX 4070 Ti Super 16GB run Granite 3.1 8B?

Yes, RTX 4070 Ti Super 16GB can run Granite 3.1 8B with a B grade (Runs well). Expected decode speed: 112.0 tok/s.

How much VRAM does Granite 3.1 8B need?

Granite 3.1 8B (8B parameters) requires approximately 9.6 GB of memory with Q4_K_M quantization.

What is the best quantization for Granite 3.1 8B?

The recommended quantization for Granite 3.1 8B is Q4_K_M, which balances quality and memory efficiency.

What speed will Granite 3.1 8B run at on RTX 4070 Ti Super 16GB?

On RTX 4070 Ti Super 16GB, Granite 3.1 8B achieves approximately 112.0 tokens per second decode speed with a time-to-first-token of 1729ms using Q4_K_M quantization.

Can RTX 4070 Ti Super 16GB run Granite 3.1 8B for coding?

For coding workloads, Granite 3.1 8B on RTX 4070 Ti Super 16GB receives a B grade with 112.0 tok/s and 68K context.

What context window can Granite 3.1 8B use on RTX 4070 Ti Super 16GB?

On RTX 4070 Ti Super 16GB, Granite 3.1 8B can safely use up to 68K tokens of context. The model's official context limit is 128K, but available memory constrains the safe maximum.

See all results for RTX 4070 Ti Super 16GB See all hardware for Granite 3.1 8B

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