Can NVIDIA RTX 4070 Ti Super (16GB) run Mistral-Small-3.1-24B-Instruct?

NVIDIA RTX 4070 Ti Super (16GB) fits Mistral-Small-3.1-24B-Instruct smoothly in GGUF Q4_K_M / AWQ (4.83 bpw). Unquantized FP16 (46.15 GB) exceeds 16 GB, but 4-bit Q4_K_M requires 15.28 GB (96% VRAM utilization) at ~32.4 tokens/sec.

How much VRAM does Mistral-Small-3.1-24B-Instruct require in Q4_K_M, FP8, and FP16?

At 8,192 context length, Mistral-Small-3.1-24B-Instruct requires 15.28 GB in GGUF Q4_K_M, 24.89 GB in FP8, and 46.15 GB in FP16.

✅ PASST IN DEN VRAM (GGUF Q4_K_M / AWQ (4.83 bpw))

Mistral-Small-3.1-24B-Instruct on NVIDIA RTX 4070 Ti Super (16GB VRAM)

Unquantized FP16 (46.15 GB) exceeds 16 GB, but 4-bit Q4_K_M requires 15.28 GB (96% VRAM utilization) at ~32.4 tokens/sec.

🎛️ Interaktiver VRAM-, Kontext- & Offload-Konfigurator

Quantisierungsformat (Reale Bits pro Gewicht)

Inferenz-Engine

Kontextfenster (Tokens) 8,192 tokens

Gleichzeitige Batch-Größe 1

KV-Cache Präzision

Anzahl der GPUs (Tensor-Parallelität)

CPU-RAM Offload (% der Layer auf CPU) 0% (100% GPU)

📊 Echtzeit-Speicher- & Performance-Telemetrie

15.28 GB / 16 GB

🟦 Modellgewichte: 13.54 GB 🟪 KV-Cache: 1.25 GB 🟨 Engine-Puffer: 0.49 GB

Benötigter GPU-VRAM15.28 GB

Benötigter System-RAM (CPU)0.00 GB

Verfügbarer Hardware-VRAM16 GB (1x NVIDIA RTX 4070 Ti Super)

VRAM-Reserve / Defizit0.72 GB

Geschätzte Decode-Geschwindigkeit~32.4 tokens/sec

Geschätzte Prefill-Geschwindigkeit~972 tokens/sec

Time-to-First-Token (4K Prompt)~4215 ms

Transformer-TopologieDense-GQA (40L, 8/32 KV/Q heads)

🚀 Startbefehl (vLLM / llama.cpp / Ollama)

ollama run mistral-small:24b
# Or llama.cpp server (8K context, Flash Attention):
llama-server -hf mistralai/Mistral-Small-3.1-24B-Instruct-2503-GGUF -c 8192 -ngl 99 -fa

CLOUD-GPU PREISVERGLEICH & MIETEN-VS-KAUFEN RECHNER

Nicht genug lokaler VRAM? Cloud-GPU in 60 Sekunden starten (Mistral-Small-3.1-24B-Instruct: 15.28 GB in 4-bit)

Recommended Cloud Tier: RTX 4090 (24GB) · Full FP16 Needs: 46.15 GB

Geschätzte Nutzungsstunden pro Tag: 4 hrs/day

Amortisationszeit (Kauf vs. Miete): ~32 months (vs $799 hardware)

Cloud Provider	RTX 4090 (24GB)	A100 (80GB)	H100 (80GB)	Est. Monthly Cost (4h/day)	Free Credit / Trial	Deploy
RunPod	$0.44 / hr	$1.39 / hr	$2.39 / hr	$52.80 / mo	Instant PyTorch / vLLM Templates + Serverless GPU	Auf RunPod starten →
Vast.ai	$0.29 / hr	$0.95 / hr	$1.79 / hr	$34.80 / mo	Save 50–70% on RTX 4090 / 5090 / A100 rentals	Spot-GPU mieten →
Vultr Cloud GPU	—	$1.79 / hr	$2.69 / hr	$214.80 / mo (A100)	$300 Free Credit	$300 Guthaben sichern →
DigitalOcean GPU	—	—	$2.99 / hr	$358.80 / mo (H100)	$200 Free Credit (60d)	$200 Startguthaben →

Alle Quantisierungsstufen im Detail: Mistral-Small-3.1-24B-Instruct × NVIDIA RTX 4070 Ti Super

Quantization Format	Core / Emb BPW	Weights (GiB)	KV Cache (8K)	Total VRAM	1x NVIDIA RTX 4070 Ti Super (16GB)	Decode / Prefill	Min GPUs
FP16 / BF16 (Unquantized)	16 / 16	43.96 GB	1.25 GB	45.7 GB	Nein (+29.7GB)	~1.8 / 972 tok/s	3x
FP8 (W8A8 / E4M3)	8.25 / 8.5	22.71 GB	1.25 GB	24.45 GB	Nein (+8.4GB)	~5.7 / 972 tok/s	2x
GGUF Q8_0 (8.50 bpw)	8.5 / 8.5	23.35 GB	1.25 GB	25.09 GB	Nein (+9.1GB)	~5.3 / 972 tok/s	2x
GGUF Q6_K (6.56 bpw)	6.56 / 6.56	18.02 GB	1.25 GB	19.76 GB	Nein (+3.8GB)	~10.4 / 972 tok/s	2x
GGUF Q5_K_M (5.69 bpw)	5.69 / 6.56	15.77 GB	1.25 GB	17.51 GB	Nein (+1.5GB)	~17.3 / 972 tok/s	2x
GGUF Q4_K_M / AWQ (4.83 bpw)	4.83 / 6.56	13.54 GB	1.25 GB	15.28 GB	KNAPP (<12% RESERVE)	~32.4 / 972 tok/s	1x
GGUF IQ4_XS (4.25 bpw)	4.25 / 5.5	11.87 GB	1.25 GB	13.61 GB	Ja (85%)	~36.8 / 972 tok/s	1x
GGUF Q3_K_M (3.91 bpw)	3.91 / 5	10.91 GB	1.25 GB	12.65 GB	Ja (79%)	~40 / 972 tok/s	1x

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