Will It Run AI

Can Hermes 2 Pro Llama 3 8B run on RTX 3050 8GB?

YES — With Offload

C50Usable
Estimated from fit model

Hermes 2 Pro Llama 3 8B needs ~7.8 GB VRAM. RTX 3050 8GB has 8.0 GB. With Q4_K_M quantization, expect ~30 tok/s.

Runtime: OllamaCapacity: OffloadBandwidth: Very lowStack: BasicBottleneck: Memory bandwidth
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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) — 7.8 GB, 30.3 tok/s, Runs with offload
7.8 GB required8.0 GB available
98% VRAM used

Fit status

Runs with offload

Decode

30.3 tok/s

TTFT

6390 ms

Safe context

19K

Memory

7.8 GB / 8.0 GB

Memory breakdown

Weights4.9 GB
KV Cache0.9 GB
Runtime1.2 GB
Headroom0.8 GB

See how fast it feels

See how fast it feelsHermes 2 Pro Llama 3 8B on RTX 3050 8GB
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: 30.3 tok/s decode · 6.4s TTFT (warm) · 76 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

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.

Best improvement path

Buy headroom, not only minimum fit

A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

Performance by workload

WorkloadGradeFitDecodeTTFTContext
ChatCTight fit30.3 tok/s3486 ms19K
CodingCRuns with offload30.3 tok/s6390 ms19K
Agentic CodingDVery compromised (needs ~0.4 GB host RAM)18.8 tok/s14985 ms19K
ReasoningCRuns with offload30.3 tok/s7552 ms19K
RAGDVery compromised (needs ~0.4 GB host RAM)18.8 tok/s18731 ms19K

Quantization options

How Hermes 2 Pro Llama 3 8B (8B params) fits at each quantization level on RTX 3050 8GB (8.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
3.1 GB
LowC54
Q3_K_S
3
3.9 GB
LowC53
NVFP4
4
4.5 GB
MediumC53
Q4_K_MBest for your GPU
4
4.9 GB
MediumC53
Q5_K_M
5
5.8 GB
HighF0
Q6_K
6
6.6 GB
HighF0
Q8_0
8
8.6 GB
Very HighF0
F16
16
16.4 GB
MaximumF0

Get started

Copy-paste commands to run Hermes 2 Pro Llama 3 8B on your machine.

Run

lms load hf-nousresearch--hermes-2-pro-llama-3-8b-gguf && lms server start

Upgrade-Optionen

Hardware, die Hermes 2 Pro Llama 3 8B gut ausführt

NVIDIARTX 3060 12GBGünstige Wahl
12 GB VRAM (+4)360 GB/s (+136)
C
Raises estimated decode speed by about 61%.48.7 tok/s Dekodierung

Raises estimated decode speed by about 61%.

Adds memory headroom for longer context windows and future model growth.

ca. $329 MSRP

NVIDIARTX 5060 Ti 16GBBestes Preis-Leistungs-Verhältnis
16 GB VRAM (+8)448 GB/s (+224)
C
Raises estimated decode speed by about 88%.56.9 tok/s Dekodierung

Raises estimated decode speed by about 88%.

Adds memory headroom for longer context windows and future model growth.

ca. $449 MSRP

NVIDIARTX 4060 Ti 16GBNVIDIA-Upgrade
16 GB VRAM (+8)288 GB/s (+64)
C
Raises estimated decode speed by about 42%.43.1 tok/s Dekodierung

Raises estimated decode speed by about 42%.

Adds memory headroom for longer context windows and future model growth.

ca. $499 MSRP

Frequently asked questions

Can RTX 3050 8GB run Hermes 2 Pro Llama 3 8B?

Yes, RTX 3050 8GB can run Hermes 2 Pro Llama 3 8B with a C grade (Runs with offload). Expected decode speed: 30.3 tok/s.

How much VRAM does Hermes 2 Pro Llama 3 8B need?

Hermes 2 Pro Llama 3 8B (8B parameters) requires approximately 7.8 GB of memory with Q4_K_M quantization.

What is the best quantization for Hermes 2 Pro Llama 3 8B?

The recommended quantization for Hermes 2 Pro Llama 3 8B is Q4_K_M, which balances quality and memory efficiency.

What speed will Hermes 2 Pro Llama 3 8B run at on RTX 3050 8GB?

On RTX 3050 8GB, Hermes 2 Pro Llama 3 8B achieves approximately 30.3 tokens per second decode speed with a time-to-first-token of 6390ms using Q4_K_M quantization.

Can RTX 3050 8GB run Hermes 2 Pro Llama 3 8B for coding?

For coding workloads, Hermes 2 Pro Llama 3 8B on RTX 3050 8GB receives a C grade with 30.3 tok/s and 19K context.

What context window can Hermes 2 Pro Llama 3 8B use on RTX 3050 8GB?

On RTX 3050 8GB, Hermes 2 Pro Llama 3 8B can safely use up to 19K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

What should I upgrade first if Hermes 2 Pro Llama 3 8B feels slow on RTX 3050 8GB?

Buy headroom, not only minimum fit. A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

See all results for RTX 3050 8GBSee all hardware for Hermes 2 Pro Llama 3 8B
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