H2ogpt 4096 Llama2 70B Chat — Hardware Requirements & GPU Compatibility
Chat
H2ogpt 4096 Llama2 70B Chat is a 69.0B-parameter open language model from h2oai in the Llama 2 family. It supports a context window of up to 4,096 tokens. At Q4_K_M it needs about 42.36 GB of VRAM — see which GPUs and Macs can run it below.
Doesn't fit a 24 GB GPU
Smallest consumer fit → RTX 5090 · Q2_K · 31 GB · ~39 tok/s
Macs from 36 GB (Q2_K · ~11 tok/s)
Llama 2 Community
Not set up for it?
Reference GPU · RTX 4090 24 GB
Estimates, ±20% · how we estimate
Run H2ogpt 4096 Llama2 70B Chat
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
vllm serve h2oai/h2ogpt-4096-llama2-70b-chat --max-model-len 4096vLLM repo with its own quantisation
Download: Weights on Hugging Face
Ways to runOther ways
each opens step-by-stepRun in cloud
Runs on A100 80GB (37 GB headroom) · Q4_K_M
- Generation speed
- ~31 tok/s
- generation speed
- Cost per 1M output tokens
- $10.21
- per 1M output tokens
How Much VRAM Does H2ogpt 4096 Llama2 70B Chat Need?
Select a quantization level; the listed hardware adheres to it.
| Quantization | VRAM | +32K | On RTX 4090 (reference) | |
|---|---|---|---|---|
| 30.3 GB | — | 2-bit · K-quant — 2-bit quantization with K-quant improvements | Does not fit: needs 30.3 GB | |
| 34.6 GB | — | 3-bit medium — 3-bit medium quantization | Does not fit: needs 34.6 GB | |
| 42.4 GB | — | 4-bit medium — 4-bit medium quantization — most popular sweet spot | Does not fit: needs 42.4 GB | |
| 50.1 GB | — | 5-bit medium — 5-bit medium quantization — good quality/size tradeoff | Does not fit: needs 50.1 GB | |
| 57.9 GB | — | 6-bit, very good — 6-bit quantization, very good quality | Does not fit: needs 57.9 GB | |
| 70.0 GB | — | 8-bit, near-lossless — 8-bit quantization, near-lossless | Does not fit: needs 70 GB | |
| 138.9 GB | — | 16-bit · full — Full half-precision — baseline for inference | Does not fit: needs 138.9 GB |
Selected Q4_K_M
4-bit medium quantization43.0GBat 4K context
- Weights
- 41.4 GB
- KV cache
- 1.3 GB
- Overhead
- 0.3 GB
Doesn't fit RTX 4090 (reference) · needs 19.0 GB more
Or .
Which Hardware Can Run H2ogpt 4096 Llama2 70B Chat?
Q4_K_M · 42.4 GBH2ogpt 4096 Llama2 70B Chat (Q4_K_M) requires 42.4 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 56+ GB is recommended. Using the full 4K context window can add up to 0.7 GB, bringing total usage to 43.0 GB. No consumer GPU has enough memory. 67 devices with unified memory can run H2ogpt 4096 Llama2 70B Chat, including Mac Studio (M3 Ultra, 256GB), Mac Studio (M3 Ultra, 512GB), Mac Pro M2 Ultra (64 GB).
32–40 GB
Q4_K_M · best fit Q2_K (31.0 GB)
1 GPU · 2 Mac classes · 1 device family
RTX 5090~39Simulate output on RTX 5090M5 Max36 GB~11Simulate output on Mac Studio M5 Max (36 GB)M3 Pro*36 GB~4Simulate output on MacBook Pro 14" M3 Pro (36 GB)Orin~4Simulate output on NVIDIA Jetson AGX Orin 32GB48 GB
48 GB tier →fits · 5.0 GB free · up to 4K 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)~4Simulate output on Snapdragon X2 Elite Extreme Copilot+ PC
By VRAM tier · typical GPUs and Macs at Q4_K_M
Alternatives to H2ogpt 4096 Llama2 70B Chat
Llama 2 family · 11 models →Similar models
6| Model | Score | VRAM | tok/s | Why it is similar |
|---|---|---|---|---|
| Llama 3.3 70B InstructMeta | 80.0 | 51.3 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
- h2oai
- Family
- Llama 2
- Parameters
- 69.0B
- Architecture
- LlamaForCausalLM
- Context Length
- 4,096 tokens
- Vocabulary Size
- 32,000
- Release Date
- 2023-08-09
- License
- Llama 2 Community
Frequently Asked Questions
How much VRAM does H2ogpt 4096 Llama2 70B Chat need?
H2ogpt 4096 Llama2 70B Chat requires 42.4 GB of VRAM at Q4_K_M, or 138.9 GB at FP16. Full 4K context adds up to 0.7 GB (43.0 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 69.0B × 4.8 bits ÷ 8 = 41.4 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 ≈ 1.6 GB (at full 4K context)
VRAM usage by quantization
Can NVIDIA GeForce RTX 5090 run H2ogpt 4096 Llama2 70B Chat?
Yes, at Q2_K (30.3 GB) or lower. Higher quantizations like Q3_K_M (34.6 GB) exceed the NVIDIA GeForce RTX 5090's 32 GB.
What's the best quantization for H2ogpt 4096 Llama2 70B Chat?
For H2ogpt 4096 Llama2 70B Chat, Q4_K_M (42.4 GB) offers the best balance of quality and VRAM usage. Q5_K_M (50.1 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 30.3 GB.
VRAM requirement by quantization
★ Recommended — best balance of quality and VRAM usage.
Can I run H2ogpt 4096 Llama2 70B Chat on a Mac?
Yes — Mac Studio M5 Max (36 GB) and 65 other Macs can run H2ogpt 4096 Llama2 70B Chat, the smallest at Q2_K. Apple Silicon uses unified memory, so the model shares RAM with the system. At Q4_K_M you need at least 42.4 GB of usable unified memory (RAM minus macOS overhead), from Mac Studio M5 Max (48 GB).
Can I run H2ogpt 4096 Llama2 70B Chat locally?
Yes — H2ogpt 4096 Llama2 70B Chat can run locally on consumer hardware. Popular tools include Ollama, LM Studio, and llama.cpp.
How fast is H2ogpt 4096 Llama2 70B Chat?
At Q4_K_M, H2ogpt 4096 Llama2 70B Chat can reach ~113 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 ÷ 42.4 × 0.65 = ~123 tok/s
Estimated speed at Q4_K_M (42.4 GB)
Real-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
What's the download size of H2ogpt 4096 Llama2 70B Chat?
At Q4_K_M, the download is about 41.39 GB. The full-precision FP16 version is 137.95 GB. The smallest option (Q2_K) is 29.32 GB.
Which GPUs can run H2ogpt 4096 Llama2 70B Chat?
No single consumer GPU has enough VRAM to run H2ogpt 4096 Llama2 70B Chat at Q4_K_M (42.4 GB). Multi-GPU or professional hardware is required.
Which devices can run H2ogpt 4096 Llama2 70B Chat?
67 devices with unified memory can run H2ogpt 4096 Llama2 70B Chat at Q4_K_M (42.4 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.