Hermes 2 Theta Llama 3 70B — Hardware Requirements & GPU Compatibility
ChatFunctions
Hermes 2 Theta Llama-3 70B is Nous Research's merged model combining its own Hermes 2 Pro with Meta's Llama-3-70B-Instruct, built with Charles Goddard and Arcee AI's MergeKit tool and then further RLHF-tuned on top of the merge. It uses the ChatML prompt format and is specifically trained for function calling, structured JSON outputs, and feature extraction from retrieval-augmented (RAG) documents, aiming at agentic and tool-using workflows rather than plain chat. At 70 billion parameters it needs a multi-GPU workstation or heavy quantization to run locally. Context length is 8,192 tokens, inherited from its Llama-3 base. It is released under the Llama 3 Community License, Meta's custom license permitting commercial use below 700 million monthly active users. It was published in June 2024.
Decent on a 24 GB GPU
Runs on 24 GB (IQ2_XXS · ~32 tok/s · 8K)Macs from 32 GB (IQ2_M · ~11 tok/s)
Llama 3 Community
Reference GPU · RTX 4090 24 GB
Estimates, ±20% · how we estimate
Run Hermes 2 Theta Llama 3 70B
One recommended way. Every other method, OS and quant is in the step-by-step guide.Each way opens a step-by-step guide for your OS and quant.
Recommended
ollama run hf.co/mradermacher/Hermes-2-Theta-Llama-3-70B-i1-GGUF:Q4_K_MQ4_K_M · needs ~45.3 GB · 8K context
Download: Hermes-2-Theta-Llama-3-70B.i1-Q4_K_M.gguf42.52 GBmradermacher
Ways to runOther ways
each opens step-by-stepRun in cloud
Your own private copy · Q4_K_M · 48 GB GPU
- The easiest way to try this model
- Running in a few minutes, easy setup
- Chat right here or connect any app
$5+ bonus for new users
Runs on A100 80GB (34 GB headroom) · Q4_K_M
- Generation speed
- ~31 tok/s
- generation speed
- Cost per 1M output tokens
- $10.31
- per 1M output tokens
How Much VRAM Does Hermes 2 Theta Llama 3 70B Need?
Select a quantization level; the listed hardware adheres to it.
| Quantization | VRAM | +32K | On RTX 4090 (reference) | |
|---|---|---|---|---|
| 31.0 GB | — | 2-bit · K-quant — 2-bit quantization with K-quant improvements | Does not fit: needs 31 GB | |
| 31.8 GB | — | 3-bit small — 3-bit small quantization | Does not fit: needs 31.8 GB | |
| 35.4 GB | — | 3-bit medium — 3-bit medium quantization | Does not fit: needs 35.4 GB | |
| 36.3 GB | — | 4-bit · legacy — 4-bit legacy quantization | Does not fit: needs 36.3 GB | |
| 43.3 GB | — | 4-bit medium — 4-bit medium quantization — most popular sweet spot | Does not fit: needs 43.3 GB | |
| 51.2 GB | — | 5-bit medium — 5-bit medium quantization — good quality/size tradeoff | Does not fit: needs 51.2 GB | |
| 59.2 GB | — | 6-bit, very good — 6-bit quantization, very good quality | Does not fit: needs 59.2 GB | |
| 71.5 GB | — | 8-bit, near-lossless — 8-bit quantization, near-lossless | Does not fit: needs 71.5 GB |
Selected Q4_K_M
4-bit medium quantization45.3GBat 8K context
- Weights
- 42.3 GB
- KV cache
- 2.7 GB
- Overhead
- 0.3 GB
Doesn't fit RTX 4090 (reference) · needs 21.3 GB more, IQ2_XXS fits
Or .
Which Hardware Can Run Hermes 2 Theta Llama 3 70B?
Q4_K_M · 43.3 GBHermes 2 Theta Llama 3 70B (Q4_K_M) requires 43.3 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 57+ GB is recommended. Using the full 8K context window can add up to 2.0 GB, bringing total usage to 45.3 GB. No consumer GPU has enough memory. 67 devices with unified memory can run Hermes 2 Theta Llama 3 70B, including Mac Studio (M3 Ultra, 256GB), Mac Studio (M3 Ultra, 512GB), Mac Pro M2 Ultra (64 GB).
≤ 20 GB
≤ 20 GB tier →doesn't fit · smallest quant needs 22.4 GB
32–40 GB
Q4_K_M · best fit IQ3_XXS (30.3 GB)
1 GPU · 3 Mac classes · 1 device family
RTX 5090~41Simulate output on RTX 5090M5 Max36 GB~11Simulate output on Mac Studio M5 Max (36 GB)M532 GB~4Simulate output on MacBook Air 13" M5 (32 GB)Orin~5Simulate output on NVIDIA Jetson AGX Orin 32GB48 GB
48 GB tier →fits · 2.7 GB free · up to 8K ctx
2 Mac classes · 1 device family
M5 Max48 GB~10Simulate output on Mac Studio M5 Max (48 GB)M5 Pro48 GB~5Simulate output on Mac Mini M5 Pro (48 GB)Snapdragon X2 Elite Extreme (X2E-96-100)~3Simulate output on Snapdragon X2 Elite Extreme Copilot+ PC
By VRAM tier · typical GPUs and Macs at Q4_K_M
How good is Hermes 2 Theta Llama 3 70B?
llmrun Score blends public benchmark boards into one number; 100 = the average open model.
Not enough data yet for an llmrun Score.
A Score needs results on enough independent boards. How the Composite Score is built
Benchmarks
Here are all 3 boards that have results for this model, sorted by their weight in the llmrun Score; badges show who ran the test.
- GPQA Diamond
Epoch AI · Jun 2024Independent
Independent37.5%
#75/100 open
- AIME 2024/2025
Epoch AI · Jun 2024Independent
Independent2.5%
#76/88 open
- MATH Level 5
Epoch AI · Jun 2024Independent
Independent22.7%
#29/39 open
Alternatives to Hermes 2 Theta Llama 3 70B
Llama 3 family · 65 models →Similar models
6| Model | Score | VRAM | tok/s | Why it is similar |
|---|---|---|---|---|
| NVIDIA Nemotron 3.5 Bolt 74B A9B BF16NVIDIA | — | 54.2 GB | —needs > 24 GB | same size |
| Qwen2.5 VL 72B InstructAlibaba | — | 49.7 GB | —needs > 24 GB | same size |
| Qwen2 VL 72B InstructAlibaba | — | 49.7 GB | —needs > 24 GB | same size |
| Qwen1.5 72B ChatAlibaba | — | 86.6 GB | —needs > 24 GB | same size |
| CodeLlama 70B Instruct HFMeta | — | 23.2 GB | ~29 | same size |
| StableBeluga2Stability AI | — | 23.5 GB | ~29 | same size |
Specifications
- Publisher
- Nous Research
- Family
- Llama 3
- Parameters
- 70.6B
- Architecture
- LlamaForCausalLM
- Context Length
- 8,192 tokens
- Vocabulary Size
- 128,256
- Release Date
- 2024-06-14
- License
- Llama 3 Community
Frequently Asked Questions
How much VRAM does Hermes 2 Theta Llama 3 70B need?
Hermes 2 Theta Llama 3 70B requires 43.3 GB of VRAM at Q4_K_M, or 142.1 GB at BF16. Full 8K context adds up to 2.0 GB (45.3 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 70.6B × 4.8 bits ÷ 8 = 42.3 GB
KV Cache + Overhead ≈ 1 GB (at 2K context + ~0.3 GB framework)
Fit ratings and hardware model lists check this model with room for a 16K-token context, which needs a little more memory.
KV Cache + Overhead ≈ 3 GB (at full 8K context)
VRAM usage by quantization
Can NVIDIA GeForce RTX 4090 run Hermes 2 Theta Llama 3 70B?
Yes, at IQ2_S (23.0 GB) or lower. Higher quantizations like IQ2_M (24.8 GB) exceed the NVIDIA GeForce RTX 4090's 24 GB.
What's the best quantization for Hermes 2 Theta Llama 3 70B?
For Hermes 2 Theta Llama 3 70B, Q4_K_M (43.3 GB) offers the best balance of quality and VRAM usage. Q4_K_L (44.2 GB) provides better quality if you have the VRAM. The smallest option is IQ2_XXS at 20.4 GB.
VRAM requirement by quantization
★ Recommended — best balance of quality and VRAM usage.
Can I run Hermes 2 Theta Llama 3 70B on a Mac?
Yes — Mac Studio M1 Max (32 GB) and 84 other Macs can run Hermes 2 Theta Llama 3 70B, the smallest at IQ2_M. Apple Silicon uses unified memory, so the model shares RAM with the system. At Q4_K_M you need at least 43.3 GB of usable unified memory (RAM minus macOS overhead), from Mac Studio M5 Max (48 GB).
Can I run Hermes 2 Theta Llama 3 70B locally?
Yes — Hermes 2 Theta Llama 3 70B can run locally on consumer hardware. Popular tools include Ollama, LM Studio, and llama.cpp.
How fast is Hermes 2 Theta Llama 3 70B?
At Q4_K_M, Hermes 2 Theta Llama 3 70B can reach ~111 tok/s on AMD Instinct MI350X. Speed depends mainly on GPU memory bandwidth. Real-world results typically within ±20%.
tok/s = (bandwidth GB/s ÷ model GB) × efficiency
Example: NVIDIA B200 → 8000 ÷ 43.3 × 0.65 = ~120 tok/s
Estimated speed at Q4_K_M (43.3 GB)
Real-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
What's the download size of Hermes 2 Theta Llama 3 70B?
At Q4_K_M, the download is about 42.33 GB. The full-precision BF16 version is 141.11 GB. The smallest option (IQ2_XXS) is 19.40 GB.
Which GPUs can run Hermes 2 Theta Llama 3 70B?
No single consumer GPU has enough VRAM to run Hermes 2 Theta Llama 3 70B at Q4_K_M (43.3 GB). Multi-GPU or professional hardware is required.
Which devices can run Hermes 2 Theta Llama 3 70B?
67 devices with unified memory can run Hermes 2 Theta Llama 3 70B at Q4_K_M (43.3 GB), including ASUS Ascent GX10, Beelink GTR9 Pro (Ryzen AI Max+ 395, 128 GB), Framework Desktop (Ryzen AI Max+ 395, 128 GB), GMKtec EVO-X2 (Ryzen AI Max+ 395, 128 GB). Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.