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Short answer: use Nemotron 3.5 Bolt 74B-A9B 24GB on a system with at least 24 GB of usable NVIDIA GPU memory, such as an RTX Spark laptop, a suitable GeForce RTX PC, DGX Spark, DGX Station or an RTX PRO workstation. NVIDIA lists llama.cpp, Ollama, LM Studio, Unsloth and vLLM as supported runtimes. Use Bolt E4B 4GB for the smaller embedded and physical-agent category; its announced availability is October 23, 2026. Direct Android and iPhone deployment is not documented, and the 74B-A9B model is not officially presented as a Jetson model.
The practical decision is therefore straightforward: choose 74B-A9B when local quality and roughly 24 GB of GPU memory are available; choose E4B when your target is a lower-memory Jetson or another embedded conversational device; treat phones as unsupported for direct on-device inference unless NVIDIA later publishes a mobile package and runtime.
Nemotron 3.5 Bolt variants at a glance
Bolt is an edge-oriented branch of NVIDIA’s Nemotron 3.5 family. The two announced variants target very different hardware classes rather than representing two interchangeable quantization levels.
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| Variant | Model shape and memory target | Primary hardware | Runtime support named by NVIDIA | Availability | Best fit |
|---|---|---|---|---|---|
| Bolt 74B-A9B 24GB | 74 billion total parameters, 9 billion active; native approximately 2-bit quantization designed for a 24 GB memory budget | RTX Spark laptops, GeForce RTX PCs, DGX Spark, DGX Station and RTX PRO workstations | llama.cpp, Ollama, LM Studio, Unsloth and vLLM | Published local checkpoint | Local laptop or workstation inference |
| Bolt E4B 4GB | 4 GB-class model for fast conversational and physical agents | GeForce RTX, Jetson and RTX/DGX Spark systems | Final release documentation should be checked when the model becomes available | Scheduled for October 23, 2026 | Voice assistants, interactive characters and embedded interfaces |
The 74B-A9B model is compressed from Nemotron 3.5 Super VL. Its name does not mean that 74 billion parameters must be loaded as full-precision weights: only about 9 billion parameters are active for a given token, and the published local checkpoint uses very low-bit quantization to fit the stated memory envelope.
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Pick the model from usable GPU memory, not the product name
When 74B-A9B is the right choice
Start with the memory requirement. NVIDIA designed Bolt 74B-A9B to fit into 24 GB of memory and to target at least 80 tokens per second. Your machine must expose at least 24 GB of usable GPU memory for the model configuration you intend to run. A laptop described merely as an RTX laptop may have substantially less VRAM, so the GPU family alone is not enough to qualify it.
- Check the GPU’s usable VRAM in the operating system or vendor utility before downloading the model.
- Account for the runtime, graphics driver, display allocation and context buffers. A nominal 24 GB card can have less than 24 GB available to inference.
- Prefer a supported RTX Spark, DGX Spark, DGX Station or RTX PRO configuration when you need a predictable deployment target.
- On a GeForce RTX PC, verify the exact board memory rather than assuming that every model in the series has the same capacity.
NVIDIA’s published design target is a sizing guide, not a guarantee that every 24 GB system will sustain the same speed. Cooling, power limits, driver versions, context length and concurrent requests all change the result.
When E4B is the better fit
Bolt E4B is intended for fast conversational and physical agents where a 4 GB-class model is more practical than a 74B-class model. Typical uses include an on-device voice assistant, an interactive character, a robot or appliance interface, and other embedded experiences that need prompt responses with limited memory and power.
Its announced release date is October 23, 2026. On October 16, 2026, that date is still in the future, so a production build should not depend on an E4B checkpoint, container or conversion package until NVIDIA publishes the release materials. The final package may specify different runtime, JetPack or driver combinations than those used by the announcement.
What to prepare for a laptop or desktop deployment
Hardware checks
- GPU memory: at least 24 GB usable for the 74B-A9B 24GB configuration.
- Supported host class: RTX Spark laptop, an adequately provisioned GeForce RTX PC, DGX Spark, DGX Station or RTX PRO workstation.
- Thermal capacity: sustained generation can hold the GPU near its power and temperature limits, especially in a thin laptop. Test while plugged in and using the intended performance profile.
- Storage: reserve space for the checkpoint, runtime files, cache and any alternate checkpoint. The exact download size depends on the published format.
- Software: install a current NVIDIA driver and a runtime version that explicitly supports the checkpoint format you select.
Choose the checkpoint deliberately
NVIDIA publishes quantized and BF16 checkpoints. The technical report identifies the IQ2_XXS quantized model as the local 24 GB target. That is the starting point for a laptop or workstation deployment where memory is constrained. The BF16 checkpoint is intended for customization and requires substantially more memory than the local 24 GB configuration.
Read the license attached to the exact checkpoint before distributing it. The model family is presented as open under the NVIDIA Open Model License, but the license terms still govern use, modification, redistribution and derivative models.
Step-by-step: deploy Bolt 74B-A9B locally
1. Inventory the target machine
Record the GPU model, total and currently available VRAM, driver version, operating system, system RAM, storage capacity and cooling mode. Close other GPU-heavy applications while measuring available memory. If the available figure is below 24 GB, do not assume that system RAM will transparently solve the problem: moving weights or cache data off the GPU changes performance and may not be supported by the published optimized path.
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2. Select the published 24 GB checkpoint
For a local 24 GB deployment, select NVIDIA’s IQ2_XXS quantized checkpoint. Keep the BF16 checkpoint for a customization workflow on a machine sized for it. Verify the checkpoint name, file format, license acceptance and any access requirements on NVIDIA’s current model page before downloading.
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3. Match the runtime to the host
NVIDIA lists five local options. llama.cpp is the highlighted optimized path for RTX Spark; vLLM is the natural choice for serving on DGX Spark or another supported workstation/server configuration. Ollama and LM Studio are convenient local application layers, while Unsloth is useful when your workflow includes model customization. Use the runtime’s model-import or model-selection screen to choose the exact checkpoint; do not convert files unless the runtime documentation says that conversion is required.
4. Turn on speculative decoding when available
NVIDIA supplies a LiLiCorr speculative-decoding drafter for Bolt 74B-A9B. The drafter has 1.13 billion parameters and is intended to reduce latency at low concurrency. Enable it only in a runtime and checkpoint combination that explicitly supports the supplied drafter. If the option is absent, run the base model first rather than forcing an incompatible configuration.
5. Run a small acceptance test
Begin with a short prompt and a modest output limit. Confirm that the model loads without an out-of-memory error, produces coherent text, and releases memory cleanly between requests. Then increase prompt length and output length to the values your application will use. Record first-token latency, sustained tokens per second, peak VRAM, context length, temperature, thermals and power draw.
6. Test the real workload
Interactive chat, long-context summarization and concurrent API requests stress different parts of the system. Measure each workload separately. A laptop that feels fast for one short conversation can become memory-bound when several sessions share the GPU or when a long context occupies the cache.
Runtime selection for different hosts
| Runtime | Use it when | Important qualification |
|---|---|---|
| llama.cpp | You want the RTX Spark-optimized local path or a lightweight native inference server | NVIDIA’s cited optimized checkpoint and benchmark use this runtime |
| vLLM | You are serving requests on DGX Spark or another supported NVIDIA workstation/server | Validate the specific quantization and GPU architecture combination before production |
| Ollama | You want a simple local model-management and chat workflow | Use a model package and format that the installed Ollama release recognizes |
| LM Studio | You prefer a graphical desktop interface for local testing | Confirm that the application exposes the required quantized format and GPU offload settings |
| Unsloth | You are customizing or fine-tuning rather than only serving the published quantized model | Customization generally needs a different checkpoint and more memory than the 24 GB inference target |
Runtime names alone do not guarantee identical behavior. The same checkpoint can have different memory overhead, batching behavior and tokenizer handling across runtimes, so pin the runtime version and configuration used for your acceptance test.
How fast should Bolt 74B-A9B be?
NVIDIA’s design goal is at least 80 tokens per second for the 24 GB model. A cited result was 83.99 tokens per second using the IQ2_XXS model with LiLiCorr in llama.cpp on a Windows 11 ARM x64 RTX Spark laptop configuration. That figure is a reference measurement under a named setup, not a promise for every laptop or desktop.
For a meaningful comparison, keep the prompt, output length, context, temperature, batch size, drafter setting and power mode constant. Report first-token latency separately from generation speed. Also record whether the display is attached to the same GPU, because graphics workloads can reduce available memory and throughput.
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Deploying on Jetson and other embedded hardware
Use E4B for the embedded product category
NVIDIA positions Bolt E4B for GeForce RTX, Jetson and RTX/DGX Spark systems. Its 4 GB class is aligned with embedded conversational and physical-agent workloads. The 74B-A9B announcement does not claim Jetson support, so do not treat the 24 GB model as a Jetson option merely because both products belong to the Nemotron family.
Jetson prerequisites to plan for
NVIDIA’s Jetson guide for a Nemotron 3.5 Lightning example documents AGX Thor and AGX Orin prerequisites. Those include JetPack 7.2, NVIDIA Container Runtime, Docker, adequate storage and acceptance of the model-license terms. They are a useful starting checklist for an embedded bring-up, but they are not a guarantee that E4B will use exactly the same container, JetPack build or storage requirement.
- Identify the exact Jetson module and carrier board, then install the JetPack release required by the final E4B documentation.
- Verify that NVIDIA Container Runtime and Docker can access the GPU from a test container.
- Reserve storage for the model, container layers, tokenizer files, logs and update rollback.
- Accept the license terms for the exact checkpoint before placing it in an image or device update.
- Measure boot time, first-token latency, sustained throughput, temperature, power mode and throttling under the physical workload.
Embedded devices have stricter thermal and power limits than workstation GPUs. Test in the finished enclosure, at the intended ambient temperature and with the peripherals that will be present in the product.
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Can Nemotron 3.5 Bolt run directly on an iPhone or Android phone?
Direct phone deployment is not documented in the official material covered here. The named targets are RTX PCs and laptops, DGX Spark, DGX Station, RTX PRO workstations and Jetson. There is no documented Bolt package for Android or iOS, and no stated support for Core ML, NNAPI, Qualcomm mobile NPUs or Apple mobile NPUs.
Consequently, do not promise that Bolt runs natively on an iPhone or Android phone, and do not label a generic mobile installation as supported. A phone application can instead be designed as a client of an inference service running on a supported RTX, DGX or Jetson host, but that is a networked client/server architecture rather than on-device Bolt inference. It introduces connectivity, authentication, latency, privacy and offline-operation decisions that must be tested separately.
Production serving and customization
Customization with NeMo
NVIDIA identifies NeMo as the customization toolkit for the Nemotron family. If you need domain adaptation, supervised fine-tuning or another model change, start from the checkpoint and hardware profile appropriate to that operation rather than treating the 24 GB IQ2_XXS file as a universal training artifact.
Serving with NIM
NVIDIA NIM packages supported models as production-ready microservices with validated configurations and enterprise support. NIM microservices require an NVIDIA AI Enterprise license. That licensing requirement is separate from the NVIDIA Open Model License described for the model family.
What the Open Model License allows
The Nemotron product material says the NVIDIA Open Model License permits use, modification, distribution, commercial deployment of models and derivatives. Read the license for obligations that apply to your redistribution, hosted service, derivative weights and notices. A model license does not automatically grant rights to every dataset, voice, character or application asset used around the model.
Troubleshooting common deployment failures
Out-of-memory during model load
- Confirm usable VRAM rather than the GPU’s advertised capacity.
- Close other GPU applications and reduce the runtime’s context or cache allocation if those settings are supported.
- Use the intended IQ2_XXS 24 GB checkpoint instead of a BF16 file.
- Move to a host with more memory rather than relying on unverified CPU offload for a production target.
Generation is slower than expected
- Check whether the runtime is actually offloading all intended layers to the GPU.
- Confirm that LiLiCorr is enabled only when the runtime supports the supplied drafter.
- Run on AC power with the intended performance profile and watch for thermal throttling.
- Compare first-token latency and sustained tokens per second separately; a long prompt can make the first token slow even when decoding is fast.
The runtime rejects the checkpoint
- Verify the file format and tokenizer expected by the installed runtime version.
- Use an NVIDIA-listed runtime for the model rather than an untested conversion.
- Check the checkpoint’s current name and release notes immediately before deployment because model packaging can change.
Jetson cannot start the container
- Check the JetPack version against the final model documentation.
- Verify NVIDIA Container Runtime and Docker GPU access independently with a small test container.
- Check free storage for both the image layers and model files.
- Confirm that the license terms were accepted and that the device has the required permissions.
A product team asks for phone support
Separate a phone client from phone inference. If the requirement is offline, on-device execution, wait for an official Android or iOS package and mobile-runtime statement. If a network connection is acceptable, keep inference on a documented host and test the client protocol, latency and privacy controls as a separate system.
Quick Recap
Deployment checklist
- Choose 74B-A9B only when the target exposes at least 24 GB of usable GPU memory.
- Choose E4B for the announced embedded and physical-agent use case, subject to its final release documentation.
- Use the IQ2_XXS quantized checkpoint for the 24 GB local target; reserve BF16 for appropriately sized customization work.
- Select a runtime that NVIDIA lists for the model and pin its version for testing.
- Enable LiLiCorr only with a compatible runtime and drafter package.
- Benchmark the exact hardware, software, prompt lengths, context and concurrency that production will use.
- Do not claim direct iPhone, Android or Jetson support for 74B-A9B without a later NVIDIA release.
- For Jetson, verify JetPack, container runtime, Docker, storage and licensing requirements against the final E4B instructions.
- For commercial serving, distinguish the model license from the separate NVIDIA AI Enterprise requirement for NIM microservices.
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