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DeepSeekby DeepSeek AI

DeepSeek R1 Distill 70B (Llama) VRAM & Cloud GPU Sizer

DeepSeek reasoning tokens distilled into the Llama 3.3 70B transformer architecture. Use this interactive calculator to compute exact GPU memory requirements across FP16, FP8, INT4, QLoRA, and Full Fine-Tuning.

Inference VRAM (INT4 AWQ)
~40.66 GB
1x RTX 4090 (INT4) or 2x A100 (80GB FP16)
Native Precision (FP16 / BF16)
~136 GB
Requires 2x-4x 80GB GPUs
QLoRA 4-bit Training
~70.39 GB
4x A100 80GB (QLoRA)
Interactive VRAM Sizer & Live Cloud Pricing Matrix Active

Popular Model Presets

Auto-Architected
B
0.5B (Edge)7B14B32B70B140B+
0.55 B/param
tokens
reqs
Minimum Required VRAMActive
40.66GB
Recommended Target:46.76 GB(+15% buffer)
Suggested Minimum GPU Tier:
1x L40S / RTX 6000 Ada (48GB)

VRAM Memory Footprint Breakdown

Sum: 40.66 GB
Model Weights
36.16 GB
KV-Cache Context
2.5 GB
Activations
0.5 GB
CUDA & Runtime
1.5 GB
Estimated Decoding Speed (Tokens/sec):
A100 (80GB): 36.5 t/sH100 (80GB): 60 t/s

Live Cloud GPU Cost & Pricing Engine

23 Available Nodes

Real-time verified pricing across RunPod, Lambda Labs, Vast.ai, AWS, GCP, and specialized clouds.

Lowest Cost Compatible OptionNVIDIA L40S on RunPod (48GB VRAM)
Spot Rate$0.65 / 1h
Rent Node
Buy vs. Rent TCO LabHardware break-even simulator
Providers:
GPU & ArchitectureProvider
Total VRAM
Spot (1h)
On-Demand (1h)
Action
NVIDIA L40SPopular
Ada LovelacePCIe 4.0
RunPod
48 GB
$0.65($0.65/hr)$0.94($0.94/hr)Rent
NVIDIA L40S
Ada LovelacePCIe 4.0
Lambda Labs
48 GB
$0.69($0.69/hr)$0.99($0.99/hr)Rent
NVIDIA RTX 6000 Ada
Ada LovelacePCIe 4.0
RunPod
48 GB
$0.85($0.85/hr)$1.25($1.25/hr)Rent
2x NVIDIA RTX 4090
Ada LovelacePCIe 4.0
RunPod
48 GB(2x 24GB)
$0.88($0.88/hr)$1.38($1.38/hr)Rent
NVIDIA A100 SXM4 (80GB)Popular
AmpereNVLink-3 (600 GB/s)
Lambda Labs
80 GB
$1.25($1.25/hr)$1.69($1.69/hr)Rent
2x NVIDIA RTX 5090
BlackwellPCIe 5.0
RunPod
64 GB(2x 32GB)
$1.38($1.38/hr)$1.98($1.98/hr)Rent
NVIDIA A100 SXM4 (80GB)
AmpereNVLink-3 (600 GB/s)
RunPod
80 GB
$1.39($1.39/hr)$1.89($1.89/hr)Rent
NVIDIA A100 SXM4 (80GB)
AmpereNVLink-3 (600 GB/s)
GCP (a2-ultragpu-1g)
80 GB
$1.75($1.75/hr)$3.67($3.67/hr)Rent
4x NVIDIA RTX 4090
Ada LovelacePCIe 4.0
RunPod
96 GB(4x 24GB)
$1.76($1.76/hr)$2.76($2.76/hr)Rent
NVIDIA H100 SXM5 (80GB)
HopperNVLink-4 (900 GB/s) + InfiniBand
Nebius AI Studio
80 GB
$2.19($2.19/hr)$2.85($2.85/hr)Rent
NVIDIA H100 SXM5 (80GB)Popular
HopperNVLink-4 (900 GB/s)
Lambda Labs
80 GB
$2.29($2.29/hr)$2.99($2.99/hr)Rent
NVIDIA H100 SXM5 (80GB)
HopperNVLink-4 (900 GB/s)
CoreWeave
80 GB
$2.35($2.35/hr)$3.15($3.15/hr)Rent
NVIDIA H100 SXM5 (80GB)Popular
HopperNVLink-4 (900 GB/s)
RunPod
80 GB
$2.49($2.49/hr)$3.29($3.29/hr)Rent
4x NVIDIA RTX 5090
BlackwellPCIe 5.0
RunPod
128 GB(4x 32GB)
$2.76($2.76/hr)$3.96($3.96/hr)Rent
NVIDIA H200 SXM5 (141GB)Popular
Hopper HBM3eNVLink-4 (900 GB/s)
Lambda Labs
141 GB
$3.49($3.49/hr)$4.29($4.29/hr)Rent
8x NVIDIA A100 SXM4 (80GB)
AmpereNVLink-3 (600 GB/s)
Lambda Labs
640 GB(8x 80GB)
$9.90($9.90/hr)$13.52($13.52/hr)Rent
8x NVIDIA A100 SXM4 (80GB)
AmpereNVLink-3 (600 GB/s)
AWS (EC2 p4de.24xlarge)
640 GB(8x 80GB)
$14.50($14.50/hr)$40.97($40.97/hr)Rent
8x NVIDIA H100 SXM5 (80GB)Popular
HopperNVLink-4 (900 GB/s) + Quantum-2 IB
Lambda Labs
640 GB(8x 80GB)
$18.30($18.30/hr)$23.92($23.92/hr)Rent
8x NVIDIA H100 SXM5 (80GB)
HopperNVLink-4 (900 GB/s) + 3.2Tbps InfiniBand
RunPod
640 GB(8x 80GB)
$19.80($19.80/hr)$26.32($26.32/hr)Rent
8x NVIDIA H200 SXM5 (141GB)
Hopper HBM3eNVLink-4 (900 GB/s) + Quantum-2 IB
Lambda Labs
1128 GB(8x 141GB)
$27.50($27.50/hr)$34.32($34.32/hr)Rent
8x NVIDIA H100 SXM5 (80GB)
HopperNVLink-4 (900 GB/s) + GPUDirect-TCPX
GCP (a3-highgpu-8g)
640 GB(8x 80GB)
$32.00($32.00/hr)$87.05($87.05/hr)Rent
8x NVIDIA H100 SXM5 (80GB)
HopperNVLink-4 (900 GB/s) + 3.2Tbps EFA
AWS (EC2 p5.48xlarge)
640 GB(8x 80GB)
$38.50($38.50/hr)$98.32($98.32/hr)Rent
8x NVIDIA Blackwell B200 (192GB)Popular
BlackwellNVLink-5 (1.8 TB/s) + Quantum-X800 IB
Nebius / CoreWeave
1536 GB(8x 192GB)
$44.00($44.00/hr)$58.00($58.00/hr)Rent
Showing all 23 nodes • Scroll table vertically to view full hardware catalog
Prices updated February 2026
Technical Knowledge Hub

Frequently Asked Questions & Transformer Math

Deep-dive technical answers on KV-cache calculation, CUDA Out-Of-Memory prevention, QLoRA fine-tuning benchmarks, and cloud GPU cost optimization.

DeepSeek R1 Distill 70B (Llama) Architectural Specs & Memory Scaling

Detailed breakdown of transformer parameters, attention mechanisms, and KV-cache expansion.

Total Parameters70.6 Billion
Transformer Layers80 Layers
Attention Heads / GQA64 Q / 8 KV (8:1)
Max Native Context1,31,072 Tokens

Quantization Precision vs VRAM Footprint

Precision / Quant TypeBytes / ParamModel Weights VRAMCompatible Single GPU
FP32 (32-bit Float)4 B263.01 GBRequires Multi-GPU / A100 (80GB)
FP16 (16-bit Float)2 B131.5 GBRequires Multi-GPU / A100 (80GB)
FP8 (8-bit Float)1 B65.75 GBRequires Multi-GPU / A100 (80GB)
INT8 (8-bit Integer)1 B65.75 GBRequires Multi-GPU / A100 (80GB)
INT4 / AWQ / GPTQ0.55 B36.16 GB✓ Fits on 1x L40S / RTX 6000 (48GB)
GGUF Q4_K_M0.58 B38.14 GB✓ Fits on 1x L40S / RTX 6000 (48GB)

KV-Cache Memory Growth with Sequence Length

Context Window (Tokens)FP16 KV Cache (2 B)FP8 KV Cache (1 B - vLLM)VRAM Savings
4,096 tokens1.25 GB0.63 GB-50% reduction
8,192 tokens2.5 GB1.25 GB-50% reduction
16,384 tokens5 GB2.5 GB-50% reduction
32,768 tokens10 GB5 GB-50% reduction
65,536 tokens20 GB10 GB-50% reduction
1,31,072 tokens40 GB20 GB-50% reduction

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