Can Samantha 7B run on RTX 4000 Ada 20GB?

YES — Runs Great

B67Good

Estimated from fit model

Samantha 7B needs ~9.4 GB VRAM. RTX 4000 Ada 20GB has 20.0 GB. With Q4_K_M quantization, expect ~71 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: LowStack: 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) — 9.4 GB, 70.7 tok/s, Runs well

9.4 GB required20.0 GB available

47% VRAM used

Fit status

Runs well

Decode

70.7 tok/s

TTFT

2739 ms

Safe context

Memory

9.4 GB / 20.0 GB

Memory breakdown

Weights4.3 GB

KV Cache2.0 GB

Runtime1.2 GB

Headroom2.0 GB

See how fast it feels

See how fast it feelsSamantha 7B on RTX 4000 Ada 20GB
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: 70.7 tok/s decode · 2.7s TTFT (warm) · 177 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	70.7 tok/s	1494 ms	4K
Coding	B	Runs well	70.7 tok/s	2739 ms	4K
Agentic Coding	B	Runs well	70.7 tok/s	3983 ms	4K
Reasoning	B	Runs well	70.7 tok/s	3237 ms	4K
RAG	B	Runs well	70.7 tok/s	4979 ms	4K

Inference speed

Samantha 7B inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for Samantha 7B 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	95.2	Fits
MacBook Pro M4 Max 128GB	128 GB	Q4_K_M	94.4	Fits
MacBook Pro M4 Max 64GB	64 GB	Q4_K_M	94.4	Fits
MacBook Pro M3 Max 64GB	64 GB	Q4_K_M	60.4	Fits
RTX 3060 12GB	12 GB	Q4_K_M	59.8	Fits
MacBook Pro M1 Max 64GB	64 GB	Q4_K_M	55.4	Fits
MacBook Pro M4 Pro 48GB	48 GB	Q4_K_M	48.7	Fits
RTX 4060 8GB	8 GB	Q4_K_M	46.0	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 Samantha 7B (7B params) fits at each quantization level on RTX 4000 Ada 20GB (20.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	2.7 GB	Low	B61
Q3_K_S	3	3.4 GB	Low	B62
NVFP4	4	3.9 GB	Medium	B62
Q4_K_M	4	4.3 GB	Medium	B62
Q5_K_M	5	5.0 GB	High	B63
Q6_K	6	5.7 GB	High	B63
Q8_0	8	7.5 GB	Very High	B65
F16Best for your GPU	16	14.3 GB	Maximum	B66

Get started

Copy-paste commands to run Samantha 7B on your machine.

Run

docker run --rm -it ghcr.io/ggerganov/llama.cpp:full \
  --hf-repo "cognitivecomputations/samantha-1.1-llama-7b" \
  --hf-file "samantha-1.1-llama-7b-Q4_K_M.gguf" \
  -c 4096 -ngl 99

Frequently asked questions

Can RTX 4000 Ada 20GB run Samantha 7B?

Yes, RTX 4000 Ada 20GB can run Samantha 7B with a B grade (Runs well). Expected decode speed: 70.7 tok/s.

How much VRAM does Samantha 7B need?

Samantha 7B (7B parameters) requires approximately 9.4 GB of memory with Q4_K_M quantization.

What is the best quantization for Samantha 7B?

The recommended quantization for Samantha 7B is Q4_K_M, which balances quality and memory efficiency.

What speed will Samantha 7B run at on RTX 4000 Ada 20GB?

On RTX 4000 Ada 20GB, Samantha 7B achieves approximately 70.7 tokens per second decode speed with a time-to-first-token of 2739ms using Q4_K_M quantization.

Can RTX 4000 Ada 20GB run Samantha 7B for coding?

For coding workloads, Samantha 7B on RTX 4000 Ada 20GB receives a B grade with 70.7 tok/s and 4K context.

What context window can Samantha 7B use on RTX 4000 Ada 20GB?

On RTX 4000 Ada 20GB, Samantha 7B can safely use up to 4K tokens of context. The model's official context limit is 4K, but available memory constrains the safe maximum.

See all results for RTX 4000 Ada 20GB See all hardware for Samantha 7B

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