Mental Health Mistral 7B Instructv0.2 Finetuned v2 — Hardware Requirements & GPU Compatibility
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Mental Health Mistral 7B Instructv0.2 Finetuned v2 is a 7B-parameter open language model from GRMenon in the Mistral family. At BF16 it needs about 15.40 GB of VRAM — see which GPUs and Macs can run it below.
62 downloads
41 likesSpecifications
- Publisher
- GRMenon
- Family
- Mistral
- Parameters
- 7B
- Release Date
- 2023-12-29
- License
- Apache 2.0
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How Much VRAM Does Mental Health Mistral 7B Instructv0.2 Finetuned v2 Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| BF16est. | 16.00 | 15.4 GB | — | 14.00 GB | Brain floating point 16 — preferred for training |
est.= calculated VRAM estimate; no published GGUF file found for that quantization yet. Other rows are verified against real community uploads.
Which GPUs Can Run Mental Health Mistral 7B Instructv0.2 Finetuned v2?
BF16 · 15.4 GBMental Health Mistral 7B Instructv0.2 Finetuned v2 (BF16) requires 15.4 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 21+ GB is recommended. 26 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti, NVIDIA GeForce RTX 5080.
Runs great
— Plenty of headroom
Decent
— Enough VRAM, may be tightWhich Devices Can Run Mental Health Mistral 7B Instructv0.2 Finetuned v2?
BF16 · 15.4 GB47 devices with unified memory can run Mental Health Mistral 7B Instructv0.2 Finetuned v2, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, Mac Mini M4 (16 GB).
Runs great
— Plenty of headroomRelated Models
Frequently Asked Questions
- How much VRAM does Mental Health Mistral 7B Instructv0.2 Finetuned v2 need?
Mental Health Mistral 7B Instructv0.2 Finetuned v2 requires 15.4 GB of VRAM at BF16.
VRAM = Weights + KV Cache + Overhead
Weights = 7B × 16 bits ÷ 8 = 14 GB
KV Cache + Overhead ≈ 1.4 GB (at 2K context + ~0.3 GB framework)
VRAM usage by quantization
BF1615.4 GB- Can I run Mental Health Mistral 7B Instructv0.2 Finetuned v2 on a Mac?
Mental Health Mistral 7B Instructv0.2 Finetuned v2 requires at least 15.4 GB at BF16, which exceeds the unified memory of most consumer Macs. You would need a Mac Studio or Mac Pro with a high-memory configuration.
- Can I run Mental Health Mistral 7B Instructv0.2 Finetuned v2 locally?
Yes — Mental Health Mistral 7B Instructv0.2 Finetuned v2 can run locally on consumer hardware. At BF16 quantization it needs 15.4 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Mental Health Mistral 7B Instructv0.2 Finetuned v2?
At BF16, Mental Health Mistral 7B Instructv0.2 Finetuned v2 can reach ~286 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~43 tok/s. 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 ÷ 15.4 × 0.65 = ~338 tok/s
Estimated speed at BF16 (15.4 GB)
~338 tok/s~43 tok/s~338 tok/s~286 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of Mental Health Mistral 7B Instructv0.2 Finetuned v2?
At BF16, the download is about 14.00 GB.
- Which GPUs can run Mental Health Mistral 7B Instructv0.2 Finetuned v2?
26 consumer GPUs can run Mental Health Mistral 7B Instructv0.2 Finetuned v2 at BF16 (15.4 GB). Top options include AMD Radeon RX 7900 XTX, NVIDIA GeForce RTX 3090, NVIDIA GeForce RTX 3090 Ti, AMD Radeon RX 6800. 7 GPUs have plenty of headroom for comfortable inference.
- Which devices can run Mental Health Mistral 7B Instructv0.2 Finetuned v2?
49 devices with unified memory can run Mental Health Mistral 7B Instructv0.2 Finetuned v2 at BF16 (15.4 GB), including AMD Ryzen AI 9 HX 370 (Strix Point) Laptop, ASUS Ascent GX10, Asus ROG Flow Z13 (2025, Ryzen AI Max+ 395, 128 GB), Beelink GTR9 Pro (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.