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What the published Nemotron 3.5 quantization path does
NVIDIA’s Nemotron 3.5 Lightning recipe starts with a BF16 checkpoint, converts weights to NVFP4 and applies quantization-aware distillation. The published model uses a W4A16 four_over_six recipe: weights are represented at four-bit precision while activations remain at 16-bit precision. This is the first format to try when the checkpoint, GPU architecture and inference runtime are supported.
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Quantization reduces the storage and bandwidth required for weights, but it does not turn a model into a file that can consume the entire advertised device capacity. Runtime memory also includes scales and metadata, embeddings, the key-value (KV) cache for the active context, temporary activations, CUDA and driver allocations, TensorRT engine workspace, input and output buffers, and the operating system. A 4 GB board therefore has substantially less than 4 GB available to the model engine.
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NVIDIA NeMo’s Model Optimizer documentation lists FP8, INT8 SmoothQuant and INT4 AWQ as other supported methods. Those formats are alternatives for checkpoints and targets that do not use the published NVFP4 artifact, not interchangeable guarantees that every Nemotron 3.5 model will run on every device.
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Which format should you choose?
| Format or method | Best starting use | Important qualification |
|---|---|---|
NVFP4 W4A16 (four_over_six) |
Nemotron 3.5 Lightning when the NVIDIA checkpoint and runtime support it | The published Lightning checkpoint uses this recipe; verify exact checkpoint and hardware support before building. |
| INT4 AWQ | Other supported checkpoints where weight-only four-bit quantization is available | Use only when Model Optimizer and the target GPU list the model as compatible; quality depends on calibration and layer support. |
| FP8 | A 24 GB-class target with a compatible model and a priority on quality or throughput over minimum memory | It generally requires more weight memory than four-bit formats; confirm engine support and leave room for KV cache and workspace. |
| INT8 SmoothQuant | Targets and checkpoints for which INT8 activation-and-weight quantization is supported | Calibration and activation handling matter; compatibility is model- and architecture-specific. |
Do not select a format solely by its nominal bit width. Compare memory fit, task quality, long-context degradation, time to first token, decode speed, supported GPU architecture, JetPack and TensorRT versions, thermal behavior and reproducibility. A format that builds successfully but leaves no KV-cache headroom is not a usable deployment.
Pin the software and model before quantizing
Record the complete deployment identity before changing precision. Checkpoint support and runtime behavior change, so a result that worked with one release cannot be assumed to work after an upgrade.
- Exact Hugging Face or NVIDIA checkpoint name and immutable revision.
- Tokenizer files and tokenizer revision.
- Model license and the license status of any calibration samples.
- TensorRT Edge-LLM or TensorRT-LLM version used for export and inference.
- CUDA and JetPack versions.
- Jetson SKU, installed memory, power mode, storage and cooling configuration.
- Quantization method, calibration-data version or hash, and all build options.
Check the current TensorRT Edge-LLM support matrix for the exact Nemotron checkpoint and target architecture immediately before a build. Support is not implied by the model name alone.
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Estimate the real memory budget
Begin with compressed weight size, then add every resident and peak allocation. The useful quantity is measured peak allocation on the target board, not the size of a downloaded model file.
| Budget component | Why it matters | What to measure or control |
|---|---|---|
| Quantized weights | Four-bit, eight-bit or FP8 storage determines the baseline footprint. | Use the built engine or loaded runtime allocation, not only the checkpoint archive size. |
| Scales and metadata | Block scales, zero points, descriptors and layer metadata add overhead to compressed weights. | Inspect the exporter and engine logs for allocated weight and metadata memory. |
| Embeddings and unquantized layers | Some layers may remain at higher precision or use separate buffers. | Look for fallback or unquantized layers during export. |
| KV cache | It grows with sequence length, batch size, number of layers and concurrent requests. | Measure prompt and generation workloads at the context lengths you intend to serve. |
| Activations and I/O buffers | Prefill and decode phases have different temporary allocations. | Profile both long prompts and long generated outputs. |
| TensorRT engine workspace | Engine building and runtime tactics can require large temporary regions. | Capture peak usage during the build as well as during inference. |
| CUDA, driver and operating system | The board cannot devote all physical memory to the model. | Reserve a measured system allowance and retest after enabling services used in production. |
Reject a configuration that only fits when the device is idle. Record peak allocation during engine build, model load, prompt prefill and token generation. Keep separate numbers for a cold start and a warmed-up service because allocator behavior can differ.
Prepare calibration data that represents production
AWQ, SmoothQuant and other calibration-based paths are only as meaningful as their calibration set. Use a fixed, versioned sample whose language mix, instruction style, domain vocabulary and sequence lengths resemble production traffic. Include short prompts and the longest prompts you expect to accept; a model that matches a short calibration set can degrade when attention spans a much longer context.
- Remove secrets, personal data and material that you are not licensed to process.
- Keep the same sample and preprocessing code when comparing formats.
- Hash or otherwise identify the calibration set so another engineer can reproduce it.
- Record tokenizer revision, maximum sequence length and truncation behavior.
- Do not treat calibration as a substitute for a held-out quality evaluation.
Build the 4 GB deployment
A 4 GB device is a constrained single-request target. The safe progression is to prove that the smallest compatible checkpoint loads, then increase one variable at a time.
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four_over_sixLightning checkpoint if your Jetson SKU and TensorRT Edge-LLM release list it. If it is unavailable for your model, evaluate a supported INT4 AWQ path rather than assuming an arbitrary four-bit converter will work. - Start with batch size one and one active request. Disable concurrency while establishing a memory baseline. Additional requests multiply KV-cache and buffer requirements.
- Set the shortest useful context. Choose a limit that covers the actual prompt, system instructions and expected output. Do not reserve a large maximum context “just in case”; the KV cache is allocated or grows for that limit.
- Use a conservative generation limit. A low maximum-new-token value protects the cache while you establish stability. Raise it only after profiling peak memory.
- Build with the version-matched NVIDIA export flow. Inspect logs for unsupported or fallback layers, precision changes and workspace allocation. On low-memory Jetson Orin devices such as Orin Nano, TensorRT Edge-LLM documents that INT4 engine builds may need
--externalize-weightsto reduce build-time memory. - Measure load and inference separately. A build can fail from workspace exhaustion even when the finished engine would fit, while a successful build can still fail at a long prompt because of KV-cache growth.
- Increase only one pressure variable at a time. Test context length first, then output limit, then any concurrency. Keep the last configuration whose measured peak remains below the device budget with operational headroom.
Jetson guidance also describes Nemotron 3.5 Lightning deployment with NVFP4 and DSpark on Jetson-class systems. Treat that guidance as a versioned path: match its exporter, runtime and JetPack assumptions rather than mixing components from unrelated releases.
Build and tune the 24 GB deployment
A 24 GB-class platform can test a larger Nemotron 3.5 checkpoint, a longer context, higher concurrency or a less aggressive format, but “24 GB” is still a capacity ceiling rather than a model allocation. Reserve memory for the operating system, drivers, runtime, workspace and KV cache before selecting a precision.
- Establish an NVFP4 baseline. Use the same single-request, short-context test as on the 4 GB tier. This gives you a quality, latency and memory reference.
- Profile the intended context. Increase prompt length in steps and record peak memory during prefill and decode. Long context can consume more memory than the weights saved by a quantization change.
- Test higher precision only with headroom. Evaluate FP8 or INT8 SmoothQuant when the exact checkpoint and GPU are listed as compatible. Keep the lower-precision engine available as a fallback.
- Measure concurrency explicitly. Run two or more simultaneous requests only after a single request is stable. Record per-request latency and the total KV-cache footprint.
- Set production limits from measurements. Choose maximum context, output tokens and concurrent requests from the point where quality, latency, thermal behavior and memory remain acceptable—not from the nominal module capacity.
Validate quality, speed and thermal behavior
Quantization is a deployment trade-off, so compare formats against a BF16 or other higher-precision baseline whenever that baseline is available. Use a fixed prompt suite and retain the exact outputs or scored results.
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| Measure | How to report it |
|---|---|
| Task quality | Accuracy or task-specific score, instruction-following checks and representative qualitative failures. |
| Long-context behavior | Quality at each production context tier, not only at a short default prompt. |
| Time to first token | Report prompt length, batch size, power mode and whether engine warm-up is included. |
| Decode speed | Tokens per second at the intended context and concurrency. |
| Memory | Peak during build, load, prefill and decode, with the device’s background services running. |
| Power and temperature | Board power mode, sustained temperature and whether clocks throttle during a long generation. |
Include failure cases in the report: out-of-memory during export, unsupported layers, runtime fallback to another precision, truncated prompts, incorrect tokenizer behavior and thermal throttling. A faster first token is not an improvement if decode quality or sustained throughput fails at the required context.
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Engine build runs out of memory
Build-time workspace and tactic selection can exceed the memory needed by the final engine. On supported low-memory Orin Nano-class builds, try the documented --externalize-weights option for INT4 engines, close unrelated processes, and use the exact TensorRT Edge-LLM release required by the support matrix. If the exporter still fails, reduce the checkpoint or choose a supported build path rather than merely lowering the runtime context.
Model loads but a long prompt fails
The weights fit, but the KV cache or activations do not. Lower maximum context, batch size or output limit, then measure peak memory again. Do not infer safe context from the checkpoint file size.
Quality drops after INT4 conversion
Check calibration language, sequence lengths, tokenizer revision and layer fallbacks. Compare the same prompt suite with NVFP4, INT4 AWQ and a higher-precision baseline. If the loss is unacceptable, move to FP8 or INT8 SmoothQuant on a compatible 24 GB-class target, or retain a higher-precision layer configuration where the runtime supports it.
Runtime reports unsupported layers or precision
Confirm the checkpoint revision, GPU architecture, JetPack, CUDA and TensorRT version as one supported combination. Do not mix an exporter from one release with an engine runtime from another without documented compatibility. Recheck the current support matrix because model and runtime coverage changes.
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Profile temperature, power mode and clock throttling over a long generation. Improve cooling or select an appropriate power mode before comparing formats again. Report sustained decode speed, not only a short warm-up measurement.
Package a reproducible quantization artifact
Save enough information for another operator to rebuild the same engine:
- Checkpoint name, immutable revision, tokenizer and license.
- Quantization format and complete configuration, including the W4A16
four_over_sixchoice where applicable. - Calibration-data identifier or hash and preprocessing code version.
- Export and engine-build commands, including
--externalize-weightswhen used. - TensorRT Edge-LLM or TensorRT-LLM, CUDA and JetPack versions.
- Device SKU, installed memory, power mode, storage and cooling details.
- Prompt-suite results, peak memory logs, latency, throughput, power and temperature.
Keep the 4 GB and 24 GB profiles separate. They have different context, concurrency and thermal limits even when they use the same checkpoint.
What is and is not established for 4 GB and 24 GB devices
NVIDIA publishes the NVFP4 Lightning recipe and documents several alternative quantization methods, plus Edge-LLM build guidance for low-memory Jetson hardware. Those materials do not publish a benchmark proving that every Nemotron 3.5 variant fits a 4 GB device or a 24 GB device under a specified context and concurrency. Therefore, there is no honest universal context-length number for either tier. The practical limit is the highest measured setting that leaves headroom after weights, cache, activations, workspace, runtime and system allocations are all present.
For a 4 GB deployment, that usually means the smallest supported checkpoint, NVFP4 or another supported INT4 path, one request, short context and conservative generation limits. For a 24 GB deployment, begin with the same baseline and expand context, precision or concurrency only after profiling. Publish the exact model revision and software stack with any result so readers can reproduce the measurement.
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