Will It Run AI

Can CodeNinja 1.0 OpenChat 7B i1 run on RTX 4060 Ti 8GB?

YES — Tight Fit

C51Usable
Estimated from fit model

CodeNinja 1.0 OpenChat 7B i1 needs ~7.1 GB VRAM. RTX 4060 Ti 8GB has 8.0 GB. With Q4_K_M quantization, expect ~49 tok/s.

Runtime: OllamaCapacity: TightBandwidth: 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) — 7.1 GB, 49.2 tok/s, Tight fit
7.1 GB required8.0 GB available
89% VRAM used

Fit status

Tight fit

Decode

49.2 tok/s

TTFT

3932 ms

Safe context

34K

Memory

7.1 GB / 8.0 GB

Memory breakdown

Weights4.3 GB
KV Cache0.8 GB
Runtime1.2 GB
Headroom0.8 GB

See how fast it feels

See how fast it feelsCodeNinja 1.0 OpenChat 7B i1 on RTX 4060 Ti 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: 49.2 tok/s decode · 3.9s TTFT (warm) · 123 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

WorkloadGradeFitDecodeTTFTContext
ChatCTight fit49.2 tok/s2145 ms34K
CodingCTight fit49.2 tok/s3932 ms34K
Agentic CodingCRuns with offload49.2 tok/s5719 ms34K
ReasoningCTight fit49.2 tok/s4647 ms34K
RAGCRuns with offload49.2 tok/s7149 ms34K

Quantization options

How CodeNinja 1.0 OpenChat 7B i1 (7B params) fits at each quantization level on RTX 4060 Ti 8GB (8.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
2.7 GB
LowC53
Q3_K_S
3
3.4 GB
LowC53
NVFP4
4
3.9 GB
MediumC53
Q4_K_M
4
4.3 GB
MediumC53
Q5_K_MBest for your GPU
5
5.0 GB
HighC52
Q6_K
6
5.7 GB
HighF0
Q8_0
8
7.5 GB
Very HighF0
F16
16
14.3 GB
MaximumF0

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

Opciones de mejora

Hardware que ejecuta bien CodeNinja 1.0 OpenChat 7B i1

NVIDIARTX 3060 12GBOpción económica
12 GB VRAM (+4)360 GB/s (+72)
C
Añade margen de memoria para más contexto y para que el modelo envejezca mejor.55.6 tok/s decodificación

Añade margen de memoria para más contexto y para que el modelo envejezca mejor.

~$329 MSRP

NVIDIARTX 5070 12GBMejor relación calidad-precio
12 GB VRAM (+4)672 GB/s (+384)
C
Sube la velocidad estimada de decodificación alrededor de un 99%.98 tok/s decodificación

Sube la velocidad estimada de decodificación alrededor de un 99%.

Añade margen de memoria para más contexto y para que el modelo envejezca mejor.

~$549 MSRP

NVIDIARTX 4070 Super 12GBMejora NVIDIA
12 GB VRAM (+4)504 GB/s (+216)
C
Sube la velocidad estimada de decodificación alrededor de un 85%.90.9 tok/s decodificación

Sube la velocidad estimada de decodificación alrededor de un 85%.

Añade margen de memoria para más contexto y para que el modelo envejezca mejor.

~$599 MSRP

Frequently asked questions

Can RTX 4060 Ti 8GB run CodeNinja 1.0 OpenChat 7B i1?

Yes, RTX 4060 Ti 8GB can run CodeNinja 1.0 OpenChat 7B i1 with a C grade (Tight fit). Expected decode speed: 49.2 tok/s.

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

CodeNinja 1.0 OpenChat 7B i1 (7B parameters) requires approximately 7.1 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 RTX 4060 Ti 8GB?

On RTX 4060 Ti 8GB, CodeNinja 1.0 OpenChat 7B i1 achieves approximately 49.2 tokens per second decode speed with a time-to-first-token of 3932ms using Q4_K_M quantization.

Can RTX 4060 Ti 8GB run CodeNinja 1.0 OpenChat 7B i1 for coding?

For coding workloads, CodeNinja 1.0 OpenChat 7B i1 on RTX 4060 Ti 8GB receives a C grade with 49.2 tok/s and 34K context.

What context window can CodeNinja 1.0 OpenChat 7B i1 use on RTX 4060 Ti 8GB?

On RTX 4060 Ti 8GB, CodeNinja 1.0 OpenChat 7B i1 can safely use up to 34K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for RTX 4060 Ti 8GBSee all hardware for CodeNinja 1.0 OpenChat 7B i1
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