NVIDIA RTX 3060 12GB Cloud Pricing & Rental Rates
The RTX 3060 12GB provides entry-level VRAM for budget LLM experimentation. Suitable for 7B parameter models with quantization.
Target Workload: Budget edge inference & LLM experimentation
NVIDIA RTX 3060 12GB LLM Workload Sizing & Capacity
Max Parameter Size (Single-GPU)
12GB GDDR6 VRAM supports INT4 quantization of up to 30B-class models; FP16 limited to smaller parameter counts.
Interconnect & Tensor Parallelism
PCIe 4.0 (64 GB/s). PCIe or NVLink depending on form factor; tensor parallelism efficiency varies by interconnect.
Recommended Serving Frameworks
vLLM (PagedAttention v2), SGLang (Radix Attention), or Ollama for local deployment. TensorRT-LLM for maximum throughput on NVIDIA hardware. FlashAttention-2/3 required for FP8 inference.
Market Availability & Early Reservation Watch
No verified on-demand rental instances are currently available in public spot markets. Cloud providers are accepting private cluster reservation inquiries for Q4 2026 delivery.
Telemetry Status: Theoretical & Lab Sizing Estimates
Hardware not yet widely deployed in multi-tenant public clouds. Specifications sourced from NVIDIA architectural whitepapers and lab benchmarks.
Models That Fit NVIDIA RTX 3060 12GB (12GB GDDR6)
Deterministic VRAM calculation (FP8) via entity graph. Only editorial and enriched models shown.
| MODEL | PARAMS | CONTEXT | FP8 VRAM | FIT? | CALCULATOR |
|---|---|---|---|---|---|
| Llama 3.2 3B Instruct | 3.2B | 128K | 5.4 GB | ✓ fits | Pre-filled → |
| Llama 3.2 1B Instruct | 1B | 128K | 2.9 GB | ✓ fits | Pre-filled → |
| Qwen 2.5 Coder 7B | 7.61B | 128K | 10.4 GB | ✓ fits | Pre-filled → |
| Qwen 2.5 7B Instruct | 7.61B | 128K | 10.4 GB | ✓ fits | Pre-filled → |
| Qwen 2.5 VL 7B | 7.61B | 128K | 10.4 GB | ✓ fits | Pre-filled → |
| Gemma 2 9B | 9.24B | 8K | 11.9 GB | ✓ fits | Pre-filled → |
| SmolLM2 1.7B | 1.7B | 128K | 3.7 GB | ✓ fits | Pre-filled → |
| Mistral 7B v0.3 | 7B | 32K | 9.5 GB | ✓ fits | Pre-filled → |
| Whisper Large v3 Turbo | 3B | 128K | 5.2 GB | ✓ fits | Pre-filled → |
| MiMo V2.5 (Free) | 7B | 128K | 9.7 GB | ✓ fits | Pre-filled → |
Inference & Serving Capacity
Practical model feasibility, max batch sizes, and KV-cache retention limits for NVIDIA RTX 3060 12GB.
Llama 3.3 70B
FEASIBLERequires tensor parallelism on multi-GPU
DeepSeek 671B
OOMRequires 4-8 GPU cluster with expert parallelism
Qwen 2.5 32B
FEASIBLEFits comfortably with INT4 quantization
vLLM Throughput (FP8)
N/A — no FP8 Tensor Cores
Estimated tokens/second, single GPU, Llama-class model
Max Context Window (Llama 70B)
Not feasible — 12 GB insufficient for 70B even at INT4
Maximum context length before KV-cache eviction
Hardware Bottleneck Analysis
Whether NVIDIA RTX 3060 12GB is compute-bound (TFLOPS) or memory-bandwidth bound (GB/s) across workloads.
Bottleneck Classification
Memory-bound — GDDR6 bus cannot feed compute units even at modest batch sizes
Recommended Quantization
GGUF / AWQ — INT4 mandatory
Best Cluster Topology
PCIe Single Node — budget edge
Deep Analysis
The RTX 3060 is severely memory-bound at 12 GB VRAM. Only suitable for 7B parameter models with aggressive quantization (INT4 ~3 GB). Not viable for any 70B+ workloads.
Architecture & Die Breakdown
Architecture
Ampere GA102 — 8nm Samsung
TDP
170W
Memory Subsystem
12GB GDDR6 at 360 GB/s bandwidth. GDDR6/X provides cost-effective bandwidth for workloads that don't require HBM-level throughput.
Interconnect
PCIe 4.0 (64 GB/s). Standard PCIe bus. Suitable for single-GPU workloads or multi-GPU training with gradient accumulation.
Precision Performance
| Precision | TFLOPS | Use Case |
|---|---|---|
| FP8 | 0 | Training & inference with mixed-precision |
| FP16 | 26.4 | Full-precision training, fine-tuning, evaluation |
Break-Even ROI Calculator
Monthly hours where reserved pricing beats spot for NVIDIA RTX 3060 12GB. Above the break-even point, reserve commits save money.
Spheron
RunPod
Lambda Labs
Reserved discount: ~15% vs on-demand (range: 10–20% depending on commitment duration and provider). Break-even at 100 hours/month: if you run NVIDIA RTX 3060 12GB more than 100 hours per month, reserved pricing saves money. At 720 hours/month (24/7), reserved saves $0+/mo vs on-demand.
Why these numbers? ▾
Spot rates sourced from public cloud provider APIs (Spheron, RunPod, Lambda Labs). Verified September 2026.
Reserved discount: ~15% (range: 10–20%) — observed from provider commitment pricing. See methodology.
Break-even hours derived from observed spot-to-reserved spread across providers.
ℹ️ Why NVIDIA RTX 3060 12GB break-even hours: 100 hours/month CALCULATED · MEDIUM▾
Derived from observed spot-to-reserved price spread across Spheron, RunPod, Vast.ai, and Lambda Labs. Break-even = reserved_commitment_cost / (spot_rate - reserved_rate). Reserved discount is ~15% on average (range: 10–20% depending on commitment duration).
Source: NVIDIA RTX 3060 Consumer Specs · Verified: 2026-09-26T00:00:00Z · Refreshed daily from provider APIs and market scraping
Methodology: Derived from observed spot-to-reserved price spread across Spheron, RunPod, Vast.ai, and Lambda Labs. Break-even = reserved_commitment_cost / (spot_rate - reserved_rate). Range: 10–20% reserved discount.
ℹ️ Why NVIDIA RTX 3060 12GB FP8 throughput: N/A — no FP8 Tensor Cores BENCHMARK · MEDIUM▾
Measured via vLLM v0.6.x with PagedAttention v2 and FlashAttention-3 on Ubuntu 24.04 + CUDA 12.4. Llama-class model served at batch=1. Throughput varies with context length, batch size, KV-cache size, and engine configuration.
Source: NVIDIA RTX 3060 Consumer Specs · Verified: 2026-09-26T00:00:00Z · Benchmark testing baseline: Ubuntu 24.04, CUDA 12.4, vLLM v0.6.x (PagedAttention v2, FlashAttention-3)
Methodology: Measured via vLLM v0.6.x with PagedAttention v2 and FlashAttention-3. Throughput varies with context length, batch size, KV-cache size, and engine configuration.
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Frequently Asked Questions
How much does it cost to rent an NVIDIA RTX 3060 12GB per hour?▾
What is the monthly reserved pricing for NVIDIA RTX 3060 12GB?▾
Can an NVIDIA RTX 3060 12GB run 70B parameter LLMs?▾
Is spot pricing reliable for distributed NVIDIA RTX 3060 12GB training?▾
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