Can Antares 350M run on RTX 2060 6GB?

YES — Runs Great

C54Usable

Estimated from fit model

Antares 350M needs ~2.4 GB VRAM. RTX 2060 6GB has 6.0 GB. With Q4_K_M quantization, expect ~5 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: LowStack: BasicBottleneck: Memory bandwidth

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) — 2.4 GB, 4.9 tok/s, Runs well

2.4 GB required6.0 GB available

40% VRAM used

Fit status

Runs well

Decode

4.9 tok/s

TTFT

39510 ms

Safe context

33K

Memory

2.4 GB / 6.0 GB

Memory breakdown

Weights0.2 GB

KV Cache0.4 GB

Runtime1.2 GB

Headroom0.6 GB

See how fast it feels

See how fast it feelsAntares 350M on RTX 2060 6GB
1st promptCold start — includes initialization
>What is local AI inference?
Local AI inference means running an AI language model directly on your own hardware — your laptop, desktop, or server — instead of sending requests to a remote cloud API.

When you run inference locally the model weights are loaded into your GPU or unified memory. Each token you generate requires reading those weights from memory, so memory bandwidth is the main bottleneck for decode speed.

Key benefits of running locally:
  - Full privacy: your prompts never leave your machine
  - No per-token cost or rate limits
  - Works offline once the model is downloaded
  - Latency depends only on your hardware
2nd promptWarm — model ready, no init overhead
>How much VRAM do I need?
It depends on the model size and quantization level. A rough rule of thumb:

  Model size     Q4 (4-bit)    Q8 (8-bit)    FP16
  7B params      ~4.3 GB       ~7.5 GB       ~14 GB
  13B params     ~7.9 GB       ~13.9 GB      ~26 GB
  70B params     ~42.7 GB      ~74.9 GB      ~140 GB

Most people use 4-bit quantization (Q4_K_M) which gives 90-95% of full quality at a fraction of the memory. A 24 GB GPU can comfortably run most 7B-13B models.
Estimated: 4.9 tok/s decode · 39.5s TTFT (warm) · 12 tok/s prefill

What limits this setup

This model fits, but memory bandwidth is the part holding decode speed back.

Throughput will feel slow

Estimated decode speed is only 4.9 tok/s, so this is more of a technical fit than a comfortable daily-driver setup.

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

Prioritize bandwidth, not only capacity

If this workload feels slow, the next useful step is often a GPU tier with materially faster memory bandwidth rather than only a small bump in capacity.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	C	Runs well	4.9 tok/s	21551 ms	33K
Coding	C	Runs well	4.9 tok/s	39510 ms	33K
Agentic Coding	B	Runs well	4.9 tok/s	57469 ms	33K
Reasoning	C	Runs well	4.9 tok/s	46694 ms	33K
RAG	B	Runs well	4.9 tok/s	71837 ms	33K

Inference speed

Antares 350M inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for Antares 350M at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~7 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	6.6	Fits
RTX 4090 24GB	24 GB	Q4_K_M	5.6	Fits
RTX 4080 Super 16GB	16 GB	Q4_K_M	4.9

Quantization options

How Antares 350M (0.3499999940395355B params) fits at each quantization level on RTX 2060 6GB (6.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q1_0_G128	1.125	0.1 GB	Very Low	B63
Q2_0_G128	1.71	0.1 GB	Low	B63
Q2_K	2

Get started

Copy-paste commands to run Antares 350M on your machine.

Run

docker run --rm -it ghcr.io/ggerganov/llama.cpp:full \
  --hf-repo "fdtn-ai/antares-350m" \
  --hf-file "antares-350m-Q4_K_M.gguf" \
  -c 4096 -ngl 99

Frequently asked questions

Can RTX 2060 6GB run Antares 350M?

Yes, RTX 2060 6GB can run Antares 350M with a C grade (Runs well). Expected decode speed: 4.9 tok/s.

How much VRAM does Antares 350M need?

Antares 350M (0.3499999940395355B parameters) requires approximately 2.4 GB of memory with Q4_K_M quantization.

What is the best quantization for Antares 350M?

The recommended quantization for Antares 350M is Q4_K_M, which balances quality and memory efficiency.

What speed will Antares 350M run at on RTX 2060 6GB?

On RTX 2060 6GB, Antares 350M achieves approximately 4.9 tokens per second decode speed with a time-to-first-token of 39510ms using Q4_K_M quantization.

Can RTX 2060 6GB run Antares 350M for coding?

For coding workloads, Antares 350M on RTX 2060 6GB receives a C grade with 4.9 tok/s and 33K context.

What context window can Antares 350M use on RTX 2060 6GB?

On RTX 2060 6GB, Antares 350M can safely use up to 33K tokens of context. The model's official context limit is 33K, but available memory constrains the safe maximum.

What should I upgrade first if Antares 350M feels slow on RTX 2060 6GB?

Prioritize bandwidth, not only capacity. If this workload feels slow, the next useful step is often a GPU tier with materially faster memory bandwidth rather than only a small bump in capacity.

See all results for RTX 2060 6GB See all hardware for Antares 350M

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