DEPLOYDatabase

AI chip · NVIDIA

NVIDIA B100 (Blackwell)

Blackwell-architecture data-center GPU, 700W TDP variant. Shipped alongside B200 for existing HGX chassis compatibility.

Market position

The 700 W Blackwell for buyers who wanted to slot Blackwell into existing 700 W H100 baseboards without re-thermalising. Reduced-density option next to B200 (1000 W); mostly shipped through OEM system integrators.

DEPLOY editorial. What the vendor PDF cannot tell you.

Physical-AI cross-link

Derived from claims already on record (vendor first-party TDP, throughput, price, and the reference-design cluster registry). The arithmetic is shown per row so it can be audited. No estimated inputs.

Perf per watt
5.00 FP8 TFLOPS/W
3,500 TFLOPS ÷ 700 W = 5.00

What fits in 192 GB

Weights-only footprint for public open-weight LLMs. Pure arithmetic: params × bytes/param. Excludes KV cache and activation memory; add ~10-30% headroom for real serving. A model that does not fit at FP16 may still fit at INT8 or INT4 with quality trade-offs. Not a benchmark.

ModelParamsFP16INT8INT4
Llama 3.1 8B
dense
8 B16 GB ✓8 GB ✓4 GB ✓
Llama 3.1 70B
dense
70 B140 GB ✓70 GB ✓35 GB ✓
Llama 3.1 405B
dense
405 B810 GB ✗405 GB ✗203 GB ✗
Llama 3.3 70B
dense
70 B140 GB ✓70 GB ✓35 GB ✓
DeepSeek V3
MoE (671B total, 37B active per token)
671 B1342 GB ✗671 GB ✗336 GB ✗
DeepSeek R1
MoE (671B total, 37B active per token)
671 B1342 GB ✗671 GB ✗336 GB ✗
Qwen 2.5 7B
dense
7 B14 GB ✓7 GB ✓4 GB ✓
Qwen 2.5 72B
dense
72 B144 GB ✓72 GB ✓36 GB ✓
Mixtral 8x7B
MoE (46.7B total, 12.9B active per token)
46.7 B93 GB ✓47 GB ✓23 GB ✓
Mixtral 8x22B
MoE (141B total, 39B active per token)
141 B282 GB ✗141 GB ✓71 GB ✓
Gemma 2 27B
dense
27 B54 GB ✓27 GB ✓14 GB ✓
Command R+
dense
104 B208 GB ✗104 GB ✓52 GB ✓
Kimi K2
MoE (1T total, 32B active per token)
1000 B2000 GB ✗1000 GB ✗500 GB ✗

Math: FP16 = params × 2 bytes; INT8 = params × 1 byte; INT4 = params × 0.5 bytes. MoE models sum every expert (full weights on disk), not the per-token active subset.

Common questions

Answer-first, sourced. Every claim below traces to a specific field on this page or a cited datasheet. FAQPage schema is emitted so LLM crawlers can lift these verbatim.

How much does NVIDIA B100 (Blackwell) cost?

NVIDIA B100 (Blackwell) launch pricing is not on record in the DEPLOY registry. Vendor list prices are typically published in the vendor's own briefing rather than the datasheet.

How much memory does NVIDIA B100 (Blackwell) have?

192 GB of HBM3e at 8,000 GB/s. Vendor datasheet, first-party.

How much power does one NVIDIA B100 (Blackwell) draw?

700 W TDP (thermal design power) per chip. System-level draw is higher: see the reference-design section for rack-level kW.

Which open-weight LLMs fit on one NVIDIA B100 (Blackwell)?

Of the 13 public open-weight LLMs DEPLOY tracks: 7 fit at FP16, 9 at INT8, 9 at INT4 (weights only, excludes KV cache). The full table is above with per-model math. Sparsity, offloading and multi-GPU serving change the picture; this row is single-chip weights-only.

Key facts

Class
Compute SoC (AI accelerator)
Designer
NVIDIA
Safety-critical?
No (data-center inference / training)
Record as of
2026-08-25
Specifications (9 fields, click to expand)

Vendor datasheet figures (first-party). Dense throughput first; sparse (2:4) in parentheses where NVIDIA quotes it. The exhaustive spec sheet lives on the datasheet URL below the table: this row set covers what buyers actually compare on.

Process node
TSMC 4NP
TDP
700 W
Memory
192 GB HBM3e
Memory bandwidth
8,000 GB/s
FP16 (dense)
1,750 TFLOPS
FP8 (dense)
3,500 TFLOPS
Form factor
SXM6 (700 W envelope)
Announced
2024-03-18
Released
2024-11-01

Source: vendor datasheet

Generation

Compare with

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Sources

Adoption rows appear as we verify chip-integration claims per model. Every row is a public claim the maker or a tier-1 source has stated; verification tier (verified / reported / inferred) is shown per row. See every chip on record for the full catalog or the NVIDIA record.