AI Model Compute Planner
Estimate compute requirements, VRAM footprint, multi-GPU needs, cost, sharding behavior, and NVIDIA NIM compatibility for any LLM.
AI Model Compute Planner
Model
Architecture
Runtime
Memory
GPUs
Cost
Use Case Planner
Select an industry and use case to get recommended models and configuration.
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Model Selection?
Define the AI model you plan to analyze.
Estimated VRAM: 13.9 GB
LLaMA8B paramsfp16
B
Architecture Configuration?
Fine-tune the model architecture parameters.
Runtime Configuration?
Adjust inference and training parameters to refine memory estimates.
Vector Database?
Calculated Architecture Results?
Attn / Layer
67.11 M
FFN / Layer
117.44 M
Total Params
6.43 B
KV Cache
0.54 GB
Inf. Memory
13.90 GB
Train Memory
107.04 GB
Inference Memory?
BF16
13.90 GB
Weights
Weights
12.86 GBKV Cache
0.54 GBTraining Memory?
FP32 + ADAM
107.04 GB
Weights
25.7 GBOptimizer
51.4 GBGradients
25.7 GBActivations
2.1 GBCUDA
2.0 GBNote: Training memory includes FP32 weights, gradients, optimizer states, CUDA overhead, and an approximate activation term based on the KV cache footprint. Actual training usage may vary by implementation.
Compatible GPUs for Inference?
Data Center
NVIDIA DGX B300 – Multi-Tenant Inference Node (8× B300 SXM)High Concurrency
2100 GB
• 10RU• $38/hr• 15.1 kWHigh
Headroom
NVIDIA B300 SXM (Blackwell Ultra)High Concurrency
262.5 GB
• SXM• $4.75/hr• 1.89 kWHigh
Headroom
AMD Instinct MI350X
288 GB
• OAM• $4/hrHigh
Headroom
NVIDIA B200 NVL
192 GB
• SXM• $4.5/hrHigh
Headroom
AMD Instinct MI300X
192 GB
• OAM• $3/hrHigh
Headroom
NVIDIA H200
141 GB
• SXM• $3.2/hrHigh
Headroom
AMD Instinct MI250X
128 GB
• OAM• $2/hrHigh
Headroom
NVIDIA H100 80GB
80 GB
• PCIe/SXM• $2.5/hrHigh
Headroom
NVIDIA A100 80GB
80 GB
• SXM• $1.8/hrHigh
Headroom
Workstation
NVIDIA RTX 6000 Blackwell
64 GB
• PCIe• $1.8/hrHigh
Headroom
NVIDIA L40S
48 GB
• PCIe• $1.2/hrHigh
Headroom
NVIDIA A40
48 GB
• PCIe• $1/hrHigh
Headroom
NVIDIA A10G
24 GB
• PCIe• $0.8/hrMedium
Headroom
Consumer
NVIDIA L4
24 GB
• PCIe• $0.6/hrMedium
Headroom
NVIDIA RTX 4090 (Consumer)
24 GB
• PCIe• $0.4/hrMedium
Headroom
NVIDIA T4
16 GB
• PCIe• $0.3/hrLow
Headroom
Compatible GPUs for Training?
Data Center
NVIDIA DGX B300 – Multi-Tenant Inference Node (8× B300 SXM)High Concurrency
2100 GB
• 10RU• $38/hr• 15.1 kW5%
Utilization
AMD Instinct MI350X
288 GB
• OAM• $4/hr37%
Utilization
NVIDIA B300 SXM (Blackwell Ultra)High Concurrency
262.5 GB
• SXM• $4.75/hr• 1.89 kW41%
Utilization
NVIDIA B200 NVL
192 GB
• SXM• $4.5/hr56%
Utilization
AMD Instinct MI300X
192 GB
• OAM• $3/hr56%
Utilization
NVIDIA H200
141 GB
• SXM• $3.2/hr76%
Utilization
AMD Instinct MI250X
128 GB
• OAM• $2/hr84%
Utilization
NVIDIA H100 80GBLow Memory
80 GB
• PCIe/SXM• $2.5/hr134%
Utilization
NVIDIA A100 80GBLow Memory
80 GB
• SXM• $1.8/hr134%
Utilization
Workstation
NVIDIA RTX 6000 BlackwellLow Memory
64 GB
• PCIe• $1.8/hr167%
Utilization
NVIDIA L40SLow Memory
48 GB
• PCIe• $1.2/hr223%
Utilization
NVIDIA A40Low Memory
48 GB
• PCIe• $1/hr223%
Utilization
NVIDIA A10GLow Memory
24 GB
• PCIe• $0.8/hr446%
Utilization
Consumer
NVIDIA L4Low Memory
24 GB
• PCIe• $0.6/hr446%
Utilization
NVIDIA RTX 4090 (Consumer)Low Memory
24 GB
• PCIe• $0.4/hr446%
Utilization
NVIDIA T4Low Memory
16 GB
• PCIe• $0.3/hr669%
Utilization
Workload Characteristics?
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Standard
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10 users?
Calculated RPS0.17 req/s
(10 users × 1 req/min) ÷ 60
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Tokens per Request:768
Requests per Second:0.17
Total Tokens / Sec:128
Est. Capacity Usage0%
B300 is designed for high-concurrency and long-context inference. Lower single-model utilization reflects reserved headroom for concurrent users and KV cache growth.
Hardware Profile?
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Est. per GPU
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Per GPU instance
NVIDIA DGX B300 – Multi-Tenant Inference Node (8× B300 SXM)
KV-Cache Optimized
• Data Center• 10RU• NVIDIA• Released 2025• Power: 15.1 kW
Model memory fits on a single GPU.
Estimated VRAM usage per unit: 13.9 GB.
Estimation Results?
Based on your model, workload, and hardware configuration.
Required Hardware
?8GPUs
Latency-bound
Est. TTFT: 151ms / 3000ms
NVIDIA DGX B300 – Multi-Tenant Inference Node (8× B300 SXM)
1 per replica (for memory)
× 8 replicas (for throughput)
Monthly Compute
5,760hours
Continuous 24/7 operation
Estimated Cost
/mo
@ $38/hr/GPU
Calculation Logic
Note: GPU count increased to meet TTFT target per NVIDIA NIM guidance.
Primary Model Logic
P * bp + B * S * L * H * 2 * bp_kv
Reference: Sizer Consolidated HTML
Estimates are approximate. Actual performance varies based on specific quantization, batching strategies (e.g., vLLM, TGI), and hardware utilization.
