Memorag Mistral 7B Inst — Hardware Requirements & GPU Compatibility
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Memorag Mistral 7B Inst is a 8.6B-parameter open language model from TommyChien in the Mistral family. It supports a context window of up to 32,768 tokens. At Q4_K_M it needs about 5.72 GB of VRAM — see which GPUs and Macs can run it below.
Runs great on a 24 GB GPU
Runs from 8 GB (Q4_K_M · 16K)Best on 24 GB (Q4_K_M · ~115 tok/s · 32K)Macs from 16 GB (Q4_K_M · ~25 tok/s)
Apache 2.0
Reference GPU · RTX 4090 24 GB
Estimates, ±20% · how we estimate
Run Memorag Mistral 7B Inst
One recommended way. Every other method, OS and quant is in the step-by-step guide.
Recommended
vllm serve TommyChien/memorag-mistral-7b-inst --max-model-len 16384vLLM repo with its own quantisation
Download: Weights on Hugging Face
Other ways
each opens step-by-stepRun in cloud
Fits on RTX 3060 12GB (4 GB headroom) · Q4_K_M
- Generation speed
- ~41 tok/s
- generation speed
- Cost per 1M output tokens
- $0.59
- per 1M output tokens
How Much VRAM Does Memorag Mistral 7B Inst Need?
Select a quantization level; the listed hardware adheres to it.
| Quantization | VRAM | +32K | On RTX 4090 (reference) | |
|---|---|---|---|---|
| 4.2 GB | 8.3 GB | 2-bit · K-quant — 2-bit quantization with K-quant improvements | Fits: 32K context · ~155 tok/s | |
| 4.8 GB | 8.8 GB | 3-bit medium — 3-bit medium quantization | Fits: 32K context · ~138 tok/s | |
| 5.7 GB | 9.8 GB | 4-bit medium — 4-bit medium quantization — most popular sweet spot | Fits: 32K context · ~115 tok/s | |
| 6.7 GB | 10.7 GB | 5-bit medium — 5-bit medium quantization — good quality/size tradeoff | Fits: 32K context · ~98 tok/s | |
| 7.7 GB | 11.7 GB | 6-bit, very good — 6-bit quantization, very good quality | Fits: 32K context · ~86 tok/s | |
| 9.2 GB | 13.2 GB | 8-bit, near-lossless — 8-bit quantization, near-lossless | Fits: 32K context · ~72 tok/s | |
| 17.7 GB | 21.8 GB | 16-bit · bfloat — Brain floating point 16 — preferred for training | Fits: 32K context · ~37 tok/s |
Selected Q4_K_M
4-bit medium quantization7.6GBat 16K context
- Weights
- 5.2 GB
- KV cache
- 2.2 GB
- Overhead
- 0.3 GB
Fits RTX 4090 (reference) · 16.4 GB free · ~115 tok/s
≥50 tok/s needs ≥440 GB/s · RTX 5090 ~204, RTX 3090 Ti ~115
Which Hardware Can Run Memorag Mistral 7B Inst?
Q4_K_M · 5.7 GBMemorag Mistral 7B Inst (Q4_K_M) requires 5.7 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 8+ GB is recommended. Using the full 33K context window can add up to 4.0 GB, bringing total usage to 9.8 GB. 52 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti, NVIDIA GeForce RTX 3070 Ti. 151 devices with unified memory can run Memorag Mistral 7B Inst, including Mac Studio (M3 Ultra, 256GB), Mac Studio (M3 Ultra, 512GB), NVIDIA Jetson Orin Nano 8GB (Super).
≤ 11 GB
≤ 11 GB tier →fits · 0.4 GB free · up to 16K ctx
16 GPUs · 1 device family
RTX 3080~86Simulate output on RTX 3080RTX 2080 Ti~70Simulate output on RTX 2080 TiRTX 3070 Ti~69Simulate output on RTX 3070 TiOrin Nano~12Simulate output on NVIDIA Jetson Orin Nano 8GB (Super)20 GB
fits · 12.4 GB free · up to 32K ctx
1 GPU
RX 7900 XT~84Simulate output on RX 7900 XT32–40 GB
fits · 24.4 GB free · up to 32K ctx
1 GPU · 3 Mac classes · 4 device families
RTX 5090~204Simulate output on RTX 5090M5 Max36 GB~56Simulate output on Mac Studio M5 Max (36 GB)M532 GB~19Simulate output on MacBook Air 13" M5 (32 GB)Orin~23Simulate output on NVIDIA Jetson AGX Orin 32GBSnapdragon X Elite (X1E-80-100)~15Simulate output on Snapdragon X Elite Copilot+ PC
By VRAM tier · typical GPUs and Macs at Q4_K_M
Alternatives to Memorag Mistral 7B Inst
Mistral family · 34 models →Similar models
6| Model | Score | VRAM | tok/s | Why it is similar |
|---|---|---|---|---|
| Granite Switch 4.1 8BIBM | — | 20.7 GB | ~36 | same size |
| ALoDLM 8BAmazon | — | 7.6 GB | ~119 | same size |
| Llm Jp 4.1 8B Thinkingllm-jp | — | 7.6 GB | ~115 | same size |
| Apertus V1.5 8Bswiss-ai | — | 8.5 GB | ~111 | same size |
| LFM2.5 8B A1BLiquid AI | — | 5.6 GB | ~335 | same size |
| BitCPM CANN 8BOpenBMB | — | 5.6 GB | ~127 | same size |
Specifications
- Publisher
- TommyChien
- Family
- Mistral
- Parameters
- 8.6B
- Architecture
- MistralForCausalLM
- Context Length
- 32,768 tokens
- Vocabulary Size
- 32,000
- Release Date
- 2024-09-03
- License
- Apache 2.0
Frequently Asked Questions
How much VRAM does Memorag Mistral 7B Inst need?
Memorag Mistral 7B Inst requires 5.7 GB of VRAM at Q4_K_M, or 17.7 GB at BF16. Full 33K context adds up to 4.0 GB (9.8 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 8.6B × 4.8 bits ÷ 8 = 5.2 GB
KV Cache + Overhead ≈ 0.5 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 ≈ 4.6 GB (at full 33K context)
VRAM usage by quantization
What's the best quantization for Memorag Mistral 7B Inst?
For Memorag Mistral 7B Inst, Q4_K_M (5.7 GB) offers the best balance of quality and VRAM usage. Q5_K_M (6.7 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 4.2 GB.
VRAM requirement by quantization
★ Recommended — best balance of quality and VRAM usage.
Can I run Memorag Mistral 7B Inst on a Mac?
Yes — Mac Mini M2 Pro (16 GB) and 133 other Macs can run Memorag Mistral 7B Inst. Apple Silicon uses unified memory, so the model shares RAM with the system. At Q4_K_M you need at least 5.7 GB of usable unified memory (RAM minus macOS overhead).
Can I run Memorag Mistral 7B Inst locally?
Yes — Memorag Mistral 7B Inst can run locally on consumer hardware. Popular tools include Ollama, LM Studio, and llama.cpp.
How fast is Memorag Mistral 7B Inst?
At Q4_K_M, Memorag Mistral 7B Inst can reach ~839 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~115 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 ÷ 5.72 × 0.65 = ~909 tok/s
Estimated speed at Q4_K_M (5.7 GB)
Real-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
What's the download size of Memorag Mistral 7B Inst?
At Q4_K_M, the download is about 5.15 GB. The full-precision BF16 version is 17.17 GB. The smallest option (Q2_K) is 3.65 GB.
Which GPUs can run Memorag Mistral 7B Inst?
52 consumer GPUs can run Memorag Mistral 7B Inst at Q4_K_M. Top options include AMD Radeon RX 6700 XT, AMD Radeon RX 6800, AMD Radeon RX 6800 XT, AMD Radeon RX 7600. 40 GPUs have plenty of headroom for comfortable inference.
Which devices can run Memorag Mistral 7B Inst?
151 devices with unified memory can run Memorag Mistral 7B Inst at Q4_K_M (5.7 GB), including AMD Ryzen AI 9 HX 370 (Strix Point) Laptop, ASUS Ascent GX10, Beelink GTR9 Pro (Ryzen AI Max+ 395, 128 GB), Framework Desktop (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.