Can CodeNinja 1.0 OpenChat 7B i1 run on GTX 1060 6GB?

BARELY — Tight on Memory

D38Poor

Estimated from fit model

CodeNinja 1.0 OpenChat 7B i1 needs ~6.6 GB VRAM. GTX 1060 6GB has 6.0 GB. With Q4_K_M quantization, expect ~16 tok/s.

Runtime: llama.cppCapacity: OffloadBandwidth: Very lowStack: StandardBottleneck: 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) — 6.6 GB, 15.7 tok/s, Very compromised (needs ~0.4 GB host RAM)

6.6 GB required6.0 GB available

110% VRAM needed

0.6 GB over capacity — needs offload or smaller quantization

Fit status

Very compromised (needs ~0.4 GB host RAM)

Decode

15.7 tok/s

TTFT

12294 ms

Safe context

Memory

6.6 GB / 6.0 GB

Offload

10%

Memory breakdown

Weights4.3 GB

KV Cache0.8 GB

Runtime0.9 GB

Headroom0.6 GB

See how fast it feels

See how fast it feelsCodeNinja 1.0 OpenChat 7B i1 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: 15.7 tok/s decode · 12.3s TTFT (warm) · 39 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

Workload	Grade	Fit	Decode	TTFT	Context
Chat	C	Runs with offload (needs ~0.1 GB host RAM)	18.1 tok/s	5823 ms	4K
Coding	D	Very compromised (needs ~0.4 GB host RAM)	15.7 tok/s	12294 ms	4K
Agentic Coding	F	Too heavy	12.2 tok/s	23137 ms	4K
Reasoning	D	Very compromised (needs ~0.4 GB host RAM)	15.7 tok/s	14530 ms	4K
RAG	F	Too heavy	12.2 tok/s	28922 ms	4K

Inference speed

CodeNinja 1.0 OpenChat 7B i1 inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for CodeNinja 1.0 OpenChat 7B i1 at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~98 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	98.0	Fits
RTX 4090 24GB	24 GB	Q4_K_M	98.0	Fits
RTX 4080 Super 16GB	16 GB	Q4_K_M	98.0	Fits
RTX 3090 24GB	24 GB	Q4_K_M	98.0	Fits
RX 7900 XTX 24GB	24 GB	Q4_K_M	98.0	Fits
Mac Studio M3 Ultra 256GB	256 GB	Q4_K_M	98.0	Fits
Mac Studio M2 Ultra 128GB	128 GB	Q4_K_M	98.0	Fits
Mac Studio M1 Ultra 128GB	128 GB	Q4_K_M	98.0	Fits
RTX 4070 12GB	12 GB	Q4_K_M	88.5	Fits
MacBook Pro M4 Max 128GB	128 GB	Q4_K_M	87.8	Fits
MacBook Pro M4 Max 64GB	64 GB	Q4_K_M	87.8	Fits
MacBook Pro M3 Max 64GB	64 GB	Q4_K_M	56.2	Fits
RTX 3060 12GB	12 GB	Q4_K_M	55.6	Fits
MacBook Pro M1 Max 64GB	64 GB	Q4_K_M	51.5	Fits
RTX 4060 8GB	8 GB	Q4_K_M	46.5	Tight
MacBook Pro M4 Pro 48GB	48 GB	Q4_K_M	45.3	Fits

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 CodeNinja 1.0 OpenChat 7B i1 (7B params) fits at each quantization level on GTX 1060 6GB (6.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	2.7 GB	Low	C54
Q3_K_SBest for your GPU	3	3.4 GB	Low	C53
NVFP4	4	3.9 GB	Medium	F0
Q4_K_M	4	4.3 GB	Medium	F0
Q5_K_M	5	5.0 GB	High	F0
Q6_K	6	5.7 GB	High	F0
Q8_0	8	7.5 GB	Very High	F0
F16	16	14.3 GB	Maximum	F0

Get started

Copy-paste commands to run CodeNinja 1.0 OpenChat 7B i1 on your machine.

Run

lms load hf-mradermacher--codeninja-1-0-openchat-7b-i1-gguf && lms server start

アップグレードオプション

CodeNinja 1.0 OpenChat 7B i1を快適に動かすハードウェア

RTX 3050 8GBコスパ重視

8 GB VRAM (+2)224 GB/s (+32)

Removes host-memory offload, which is usually the single biggest latency and throughput win.29.1 tok/sデコード

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

Raises estimated decode speed by about 85%.

〜$249 MSRP

RTX 5060 8GBコスパ最良

8 GB VRAM (+2)448 GB/s (+256)

Removes host-memory offload, which is usually the single biggest latency and throughput win.64 tok/sデコード

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

Raises estimated decode speed by about 308%.

〜$299 MSRP

RTX 5050 8GBNVIDIAアップグレード

8 GB VRAM (+2)224 GB/s (+32)

Removes host-memory offload, which is usually the single biggest latency and throughput win.44.1 tok/sデコード

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

Raises estimated decode speed by about 181%.

〜$299 MSRP

Frequently asked questions

Can GTX 1060 6GB run CodeNinja 1.0 OpenChat 7B i1?

Yes, GTX 1060 6GB can run CodeNinja 1.0 OpenChat 7B i1 with a D grade (Very compromised (needs ~0.4 GB host RAM)). Expected decode speed: 15.7 tok/s.

How much VRAM does CodeNinja 1.0 OpenChat 7B i1 need?

CodeNinja 1.0 OpenChat 7B i1 (7B parameters) requires approximately 6.6 GB of memory with Q4_K_M quantization.

What is the best quantization for CodeNinja 1.0 OpenChat 7B i1?

The recommended quantization for CodeNinja 1.0 OpenChat 7B i1 is Q4_K_M, which balances quality and memory efficiency.

What speed will CodeNinja 1.0 OpenChat 7B i1 run at on GTX 1060 6GB?

On GTX 1060 6GB, CodeNinja 1.0 OpenChat 7B i1 achieves approximately 15.7 tokens per second decode speed with a time-to-first-token of 12294ms using Q4_K_M quantization.

Can GTX 1060 6GB run CodeNinja 1.0 OpenChat 7B i1 for coding?

For coding workloads, CodeNinja 1.0 OpenChat 7B i1 on GTX 1060 6GB receives a D grade with 15.7 tok/s and 4K context.

What context window can CodeNinja 1.0 OpenChat 7B i1 use on GTX 1060 6GB?

On GTX 1060 6GB, CodeNinja 1.0 OpenChat 7B i1 can safely use up to 4K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

What should I upgrade first if CodeNinja 1.0 OpenChat 7B i1 feels slow on GTX 1060 6GB?

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 GTX 1060 6GB See all hardware for CodeNinja 1.0 OpenChat 7B i1

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